All neurons can perform linearly non-separable computations

All neurons can perform linearly non-separable... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/10-539" }, "headline": "All neurons can perform linearly non-separable computations", "datePublished": "2021-07-06T12:19:34", "dateModified": "2022-06-08T12:03:17", "author": [ { "@type": "Person", "name": "Romain D. Cazé" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": "Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturation due to interacting synapses. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with interacting synapses has more computational capacity than without. Because all neurons have one or more layer, we show that all neurons can potentially implement linearly non-separable computations." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/10-539", "name": "All neurons can perform linearly non-separable computations" } } ] } Home Browse All neurons can perform linearly non-separable computations ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Cazé RD. All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.12688/f1000research.53961.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Brief Report Revised All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] Previously titled: Any neuron can perform linearly non-separable computations Romain D. Cazé https://orcid.org/0000-0002-7798-3696 Romain D. Cazé https://orcid.org/0000-0002-7798-3696 PUBLISHED 08 Jun 2022 Author details Author details CNRS IEMN UMR 8520, Villeneuve d'ascq, Haut de France, 59650, France Romain D. Cazé Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the INCF gateway. Abstract Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturation due to interacting synapses. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with interacting synapses has more computational capacity than without. Because all neurons have one or more layer, we show that all neurons can potentially implement linearly non-separable computations. READ ALL READ LESS Keywords Dendrites, computation, linearly non-separable, neuroscience Corresponding Author(s) Romain D. Cazé ( [email protected] ) Close Corresponding author: Romain D. Cazé Competing interests: No competing interests were disclosed. Grant information: This work was supported by the Centre National de la Recherche Scientifique [ANR-UWAKE]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2022 Cazé RD. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Cazé RD. All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.12688/f1000research.53961.3 ) First published: 06 Jul 2021, 10 :539 ( https://doi.org/10.12688/f1000research.53961.1 ) Latest published: 08 Jun 2022, 10 :539 ( https://doi.org/10.12688/f1000research.53961.3 ) Revised Amendments from Version 2 We changed the title from "any" to "all" to further emphasized the generality of our findings and we slightly modify the intro to insist on the breadth of our work and cite a review on glutamate spillover. We change the panel B and C of the figure to more closely follow the truth table, each panel respectively use two different interpretation of the 0s and the 1s We precised the peculiarity of equation 2 and justify it. We corrected minor typos and we want to thank the reviewer for their valuable comments which improved the quality of the manuscript. We upgraded the discussion to say that we do not use a reduction in driving force to implement sub linear summation. We changed the title from "any" to "all" to further emphasized the generality of our findings and we slightly modify the intro to insist on the breadth of our work and cite a review on glutamate spillover. We change the panel B and C of the figure to more closely follow the truth table, each panel respectively use two different interpretation of the 0s and the 1s We precised the peculiarity of equation 2 and justify it. We corrected minor typos and we want to thank the reviewer for their valuable comments which improved the quality of the manuscript. We upgraded the discussion to say that we do not use a reduction in driving force to implement sub linear summation. See the author's detailed response to the review by Athanasia Papoutsi and Spyridon Chavlis See the author's detailed response to the review by Balazs B Ujfalussy READ REVIEWER RESPONSES Introduction We show here how interaction between synapses can extend the computational capacity of all neurons, even the tiniest. We already knew that dendrites might extend the computational capacity of some pyramidal neurons. Their extended dendrites capable of dendritic spikes changed the way we saw them (see Ref. 1 for one of the first articles presenting this idea). More recently a study suggested that we should model pyramidal neurons as a two layer neural networks. 2 This theoretical model was further consolidated by experiments showing that we can see a pyramidal neuron as a collection of non-linear subunits. 3 Certain non-linearities can even allow a dendrite to implement the exclusive or (XOR). 4 Moreover, a similar kind of non-monotonic non-linearity was found in human pyramidal neurons. 5 But what about other neurons with modest dendrites incapable of spiking? Pyramidal neurons only represent a fraction of all neurons. For instance, the dendrites of cerebellar stellate cells cannot emit spikes, but they do saturate 6 and they can be decomposed into multiple independent subunits - with independent membrane voltages - turning them into two-stage units like the pyramidal neuron. 7 Previously we have shown that passive dendrites are sufficient to enable a neuron to perform linearly non-separable computations, for instance, the feature binding problem. 8 Here, we go one step further and focus on cells with a modest and passive dendritic tree. We use the fact that even in this case spatially nearby synapses can interact due to glutamate spillover (for review see Ref. 9 ). We show that these cells despite having a single voltage can compute linearly non-separable functions. In the present study. We use these neurons as the smallest common denominator, and we thus conclude that all neurons can perform linearly non-separable functions. Methods An integrate and fire neuron with interacting synapses (the SIF) We started from a leaky integrate and fire (LIF). This model has a membrane V modelled by the following equation: (1) τ d v d t = - ( v ( t ) - v E ) + R I s ( t ) With τ = 20 ms the neuron time constant, v ( t ) the membrane voltage at time t and v E = −65 mV which sets the resting membrane voltage. R = 20 MΩ is the value of the resistance and I s ( t ) models the time varying synaptic inputs current. Each time the voltage reaches V t = −62 mV a spike is triggered and the voltage is resetted to −65 mV. We used the following equation to account for the synaptic inputs. (2) I s ( t ) = ( g d 1 ( t ) + g d 2 ) ( E s − v ( t ) ) The synaptic current depends on the difference between v ( t ) the neuron voltage, equal everywhere, and E s the synaptic reversal potential (0 mV). In the present work, we have four input sources and contrary to what is done usually we have only two conductances g 1 and g 2 which collapse conductance from input 1,2 and 3,4 respectively. This account for the interaction between the input sources and do not consider them fully independent as it is usually the case. Each g i is bounded between 0 and 100 pS. Each g i jumps up instantaneously to its maximal value for each incoming input spike and decays exponentially with time constant τ s = 1 ms. In a LIF all synaptic inputs are gathered into a single umbrella and i = 1. In the present work we cluster synaptic inputs into 2 groups (one green and one blue, see Figure 1 ). We used the Brian software version 2 to carry out our simulations, the code is freely available on the git repository attached with this report. Figure 1. A dendrited integrate and fire implementing a linearly non-separable computation. (A) A leaky integrate and fire (LIF) with two saturating points, each half of the 4 synaptic inputs targets a distinct point where g locally saturates at 100 pS (B) Four stimulation episodes, filled circles stand for 50 Hz poisson spike trains while empty circles stand for no input spike train. Below, we plotted the response of the LIF (grey) and of the SIF (black) during an episode. We purposely removed the ticks label as the frequencies depend on the parameter of the model and input correlation. The parameters of the model can vary largely without affecting the observation. (C) Somatic voltage response of the SIF when filled circle means spike and empty circle means no-spike. One can observe that the SIF reproduces the truth table also in this case. Boolean algebra refresher First, let’s present Boolean functions: Definition 1. A Boolean function of n variables is a function on {0, 1} n into {0, 1}, where n is a positive integer. Importantly, we commonly assume that neurons can only implement linearly separable computations: Definition 2. f is a linearly separable computation of n variables if and only if there exists at least one vector w ∈ R n and a threshold Θ ∈ R such that: f ( X ) = 1 if w ⋅ X ≥ Θ 0 otherwise where X ∈ {0, 1} n is the vector notation for the Boolean input variables. Results The compact feature binding problem (cFBP) In this section, we demonstrate a class of compact linearly inseparable (non-separable) computations that we are going to study. These computations are compact because they only need to be defined on have four input lines. Changing the other input lines would not affect our result. We entirely specify an example in Table 1 . This computation that we call the compact feature binding problem (cFBP) is linearly inseparable. Table 1. The truth table of a linearly inseparable computation. Inputs Output 0011 0 1100 0 0101 1 1010 1 Proposition 1. The cFBP is linearly inseparable (non-separable) Proof. The output must be 0 for two disjoint couples (1,2) and (3,4) of active inputs. It means that w 1 + w 2 ≤ Θ, and w 3 + w 4 ≤ Θ, and we can add these two inequalities to obtain w 1 + w 2 + w 3 + w 4 ≤ 2Θ. However, the output must be 1 for two other couples made of the same active inputs (1,3) and (2,4). It means that w 1 + w 3 > Θ, and w 2 + w 4 > Θ, and we can add these two inequalities to obtain w 1 + w 2 + w 3 + w 4 > 2Θ. This yield a contradiction proving that no weights set exists solving this set of inequalities. The cFBP is compact beauce it specifies only four lines of a function. A complete definition would include 16 distinct input/output relationship. This incomplete definition of the function leaving all the remaining input/output relation is the minimal. This computation is as complex as the famous exclusive OR (XOR). Note here that our SIF can also implement the XOR using a parameter set explained here [?]. However, contrary to the XOR it can be implemented with excitatory inputs and a monotone transfer function. 8 We can extend the cFBP by increasing the number of inputs. In this case we deal with tuples instead of couples. As such, the cFBP corresponds to an entire family of linearly inseparable computations, and a SIF can implement them using the strategy that we will present in the next section. A LIF with its linear integration cannot implement such a computation. While a neuron with two groups of saturating synapses can easily implement it. We already proved how a ball-and-stick biophysical model can implement this computation in a previous study. 8 Implementing the cFBP in a saturating integrate and fire (SIF) We use two independently saturating conductances to implement the cFBP in a minimal extension of the LIF. The SIF has a single membrane voltage to account for its compactness so we might wonder how local saturation can arise in such a morphology. Saturation has two possible origins: (1) a reduction in driving force can cause saturation as in Ref. 6 , but (2) it can also be due to the intrinsic limitations in conductance per unit of surface. This latter possibility makes saturation possible in an electrically compact neuron. In every cases the conductance is going to reach an upper bound per unit of surface and the only possibility to increase excitation consists in stimulating a larger area. We are going to employ this local bounding of the conductance to implement the cFBP in a SIF. To do that, we only need two saturating points as shown in Figure 1A . We can interpret the 0 s and 1 s in the truth table in at least two ways: (1) either the pre- or post-synaptic neurons activates (2) or they reach a given spike frequency. In the following section, we will use the two interpretations. We first consider a pre-synaptic input active when it fires a 50 Hz poisson spike-train and inactive if it does not fire (this value is arbitrary and can largely vary to match a neuron working range). We stimulate our model in four distinct episodes to reproduce the truth table from the previous section. You can observe on Figure 1 two interpretation of the truth table: either a rate based or a spike based interpretation. In both cases we ca observe that locally bounding g enables to implement the cFBP. When g has no bound, the membrane voltage always reaches the spiking threshold at the same speed (LIF case). When we locally bound conductances the total input current is higher if inputs target two points rather than one (total g = 100 pS Vs g = 200 pS). All in all a SIF will respond differently for the clustered and scattered case while a LIF won’t. This enables a SIF to implement the cFBP while a LIF can’t. Discussion/Conclusion In this brief report, we introduced a small extension to the leaky integrate and fire neuron: a saturating integrate and fire neuron which can implement linearly non-separable computations. Moreover, we have shown here that two saturating points suffice. The SIF’s multiple distinctly bounded g underlie this ability. Importantly, a reduction in driving force is not the main actor triggering sublinear summation in a SIF. The threshold value guarantees V t = −62 mV that we are always far from the equilibrium voltage of the synapse E v = 0 mV. Furthermore a granule cell has a single membrane voltage through wich saturating groups of synapses would interactundermining their parallel processing. This would also be the case if the conductance were voltage-gated. The implementation of a linearly inseparable computation would have been impossible in a single compartment neuron because of interaction via the unique membrane potential. The usage of locally bounded g is crucial to make our prediction possible. The experiment demonstrating this prediction seems straightforward. One would need to stimulate four independent groups of mossy fibres following our different scenarios. We could then record how a group of granule cell respond. This can be done using optogenetics reporting (i.e. calcium imaging). We predict that a significant part of Granule cells might implement the cFBP. This prediction could reveal the true potential of single neurons. The next step consists of looking at the network level as already done with spiking dendrites. 10 The origin of the sublinearity might not be certain, but it would be certain that these neurons implement a linearly inseparable computation. Data availability No data are associated with this article. Software availability • Source code available from: https://github.com/rcaze/21_03Ca/tree/1 . • Archived source code: https://doi.org/10.5281/zenodo.6594665 . 11 • License: MIT license . Competing interests No competing interests were disclosed. Grant information This work was supported by the Centre National de la Recherche Scientifique [ANR-UWAKE]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgements I used “we” as science is a collective endeavour. Discussions on this topic had begun as early as 2013 with my former PhD Advisor and collaborators from Institut Pasteur Paris. I also want to acknowledge M. Humphries, F Zeldenrust, A. Foust for their valuable comments on the early draft and Ms Marini-Audouard for the proof-reading before submission. References 1. Bartlett M: The clusteron: towards a simple abstraction to a complex neuron. Advances in Neural Information Processing Systems. 4. 2. Poirazi P, Brannon T, Mel BW: Pyramidal neuron as two-layer neural network. Neuron. 2003; 37 (6): 989–999. PubMed Abstract | Publisher Full Text 3. Polsky A, Mel BW, Schiller J: Computational subunits in thin dendrites of pyramidal cells. Nat Neurosci. 2004; 7 (6): 621–627. PubMed Abstract | Publisher Full Text 4. Zador AM, Claiborne BJ, Brown TH: Nonlinear pattern separation in single hippocampal neurons with active dendritic membrane. Advances in Neural Information Processing Systems. page 8. 5. Gidon A, Zolnik TA, Fidzinski P, et al. : Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science. 2020; 367 (6473): 83–87. PubMed Abstract | Publisher Full Text 6. Abrahamsson T, Cathala L, Matsui K, et al. : Thin dendrites of cerebellar interneurons confer sublinear synaptic integration and a gradient of short-term plasticity. Neuron. 2012; 73 (6): 1159–1172. PubMed Abstract | Publisher Full Text 7. Tzilivaki A, Kastellakis G, Poirazi P: Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators. Nat Commun. 2019; 10 (1): 3664. PubMed Abstract | Publisher Full Text | Free Full Text 8. Cazé RD, Humphries M, Gutkin B: Passive dendrites enable single neurons to compute linearly non-separable functions. PLoS Comput Biol. 2013; 9 (2): e1002867. PubMed Abstract | Publisher Full Text | Free Full Text 9. Diamond J: A broad view of glutamate spillover. Nat Neurosci. 2002; 5 : 291–292. Publisher Full Text 10. Memmesheimer R-M, Timme M: Non-additive coupling enables propagation of synchronous spiking activity in purely random networks. PLoS Comput Biol. 2012; 8 (4): e1002384. PubMed Abstract | Publisher Full Text | Free Full Text 11. Dr Romain DC : rcaze/21_03Ca: F1000 v2 (Version 2). Zenodo. 2021. Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 06 Jul 2021 ADD YOUR COMMENT Comment Author details Author details CNRS IEMN UMR 8520, Villeneuve d'ascq, Haut de France, 59650, France Romain D. Cazé Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was supported by the Centre National de la Recherche Scientifique [ANR-UWAKE]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 08 Jun 2022, 10:539 https://doi.org/10.12688/f1000research.53961.3 version 2 Revised Published: 16 Sep 2021, 10:539 https://doi.org/10.12688/f1000research.53961.2 version 1 Published: 06 Jul 2021, 10:539 https://doi.org/10.12688/f1000research.53961.1 Copyright © 2022 Cazé RD. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Cazé RD. All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.12688/f1000research.53961.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 08 Jun 2022 Revised Views 0 Cite How to cite this report: Ujfalussy BB. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.134167.r140000 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v3#referee-response-140000 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 14 Jun 2022 Balazs B Ujfalussy , Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary Approved VIEWS 0 https://doi.org/10.5256/f1000research.134167.r140000 In general I accept the author's response to my comments. I have only 2 minor comments, that I list below. The authors renamed the model the Saturating Integrate and Fire, but the Caption of Fig 1 ... Continue reading READ ALL In general I accept the author's response to my comments. I have only 2 minor comments, that I list below. The authors renamed the model the Saturating Integrate and Fire, but the Caption of Fig 1 starts with "Dendrited IF". I think this should be corrected for making it consistent with the rest of the paper. In Eq. 2 g_D_1(t) has explicit temporal dependence, g_D_2 does not. Is there any reason for this or it is a simple typo? Also, I would consider denoting the two groups with roman numbers (I and II) of letters (a and b), since arabic numbers are for the inputs (1-4) which can be confusing. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Computational Neuroscience I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ujfalussy BB. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.134167.r140000 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v3#referee-response-140000 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 16 Sep 2021 Revised Views 0 Cite How to cite this report: Ujfalussy BB. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.77421.r121426 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v2#referee-response-121426 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 15 Feb 2022 Balazs B Ujfalussy , Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.77421.r121426 In this report the author extends his previous work (ref 3.) demonstrating that simple neurons with two saturating nonlinearities can implement certain non-trivial computational problems, i.e., the feature binding problem (FBP). The novelty of the current implementation is that it ... Continue reading READ ALL In this report the author extends his previous work (ref 3.) demonstrating that simple neurons with two saturating nonlinearities can implement certain non-trivial computational problems, i.e., the feature binding problem (FBP). The novelty of the current implementation is that it places the nonlinearity to the synaptic conductance term in the input instead of to the reduction of the synaptic driving force. This way the FBP can be implemented with electrically compact neurons without the need for independent electrical subunits. I have two main concerns: Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. In Eq. 1. I_s is input current and not input conductance I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. Fig 1, legend: It is not explained how reference 5 is related to the figure. It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Computational Neuroscience I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ujfalussy BB. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.77421.r121426 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v2#referee-response-121426 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 13 Jun 2022 Romain Cazé , CNRS UMR 8520, France 13 Jun 2022 Author Response Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - ... Continue reading Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - I proposed glutamate spillover as a candidate where nearby synapses interact. However, its effect on firing was never investigated to our knowledge. This work thus make a strong experimental prediction and give a computational role for glutamate spillover. I added two references to further support this argument. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. - I renamed our model the Saturating Integrate and Fire, this model account for a neuron where synapses target distinct points where they interact. I now discuss this statement extensively in our conclusion. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. - I precised that ref.6 concerns pyramidal neurons only and not granule cells. I also insist in the introduction on the fact that granule cells are isopotential structure impossibl to decompose into subunits. In Eq. 1. I_s is input current and not input conductance - I corrected the mistake I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. - I now underline this crucial difference and discuss it in our conclusion Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. - I redraw the panel B and C of this figure to make it clearer. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. - I now explicit this point in the figure legend. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. - Spikes arised when we reached -62mV Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. - I precised the reset voltage in the method section. Fig 1, legend: It is not explained how reference 5 is related to the figure. - This no longer apply in the current version of the manuscript It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. - The cFBP is compact because only four input lines need to be defined all the other remaining 12 can take any other value, we now explain this in the result section The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. - The reduction in driving force would be insufficient in an isopotential neuron as it would affect the independence of g_1 and g_2. I now further underline this point in the discussion. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. - I agree the experiment would only demonstrate that granule cells are capable of linearly inseparable computation. However, given their isopotential structure it is unlikely that saturation is due to a localized reduction in driving force. This point is now further emphasised in the discussion. Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - I proposed glutamate spillover as a candidate where nearby synapses interact. However, its effect on firing was never investigated to our knowledge. This work thus make a strong experimental prediction and give a computational role for glutamate spillover. I added two references to further support this argument. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. - I renamed our model the Saturating Integrate and Fire, this model account for a neuron where synapses target distinct points where they interact. I now discuss this statement extensively in our conclusion. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. - I precised that ref.6 concerns pyramidal neurons only and not granule cells. I also insist in the introduction on the fact that granule cells are isopotential structure impossibl to decompose into subunits. In Eq. 1. I_s is input current and not input conductance - I corrected the mistake I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. - I now underline this crucial difference and discuss it in our conclusion Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. - I redraw the panel B and C of this figure to make it clearer. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. - I now explicit this point in the figure legend. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. - Spikes arised when we reached -62mV Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. - I precised the reset voltage in the method section. Fig 1, legend: It is not explained how reference 5 is related to the figure. - This no longer apply in the current version of the manuscript It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. - The cFBP is compact because only four input lines need to be defined all the other remaining 12 can take any other value, we now explain this in the result section The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. - The reduction in driving force would be insufficient in an isopotential neuron as it would affect the independence of g_1 and g_2. I now further underline this point in the discussion. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. - I agree the experiment would only demonstrate that granule cells are capable of linearly inseparable computation. However, given their isopotential structure it is unlikely that saturation is due to a localized reduction in driving force. This point is now further emphasised in the discussion. Competing Interests: No Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 13 Jun 2022 Romain Cazé , CNRS UMR 8520, France 13 Jun 2022 Author Response Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - ... Continue reading Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - I proposed glutamate spillover as a candidate where nearby synapses interact. However, its effect on firing was never investigated to our knowledge. This work thus make a strong experimental prediction and give a computational role for glutamate spillover. I added two references to further support this argument. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. - I renamed our model the Saturating Integrate and Fire, this model account for a neuron where synapses target distinct points where they interact. I now discuss this statement extensively in our conclusion. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. - I precised that ref.6 concerns pyramidal neurons only and not granule cells. I also insist in the introduction on the fact that granule cells are isopotential structure impossibl to decompose into subunits. In Eq. 1. I_s is input current and not input conductance - I corrected the mistake I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. - I now underline this crucial difference and discuss it in our conclusion Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. - I redraw the panel B and C of this figure to make it clearer. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. - I now explicit this point in the figure legend. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. - Spikes arised when we reached -62mV Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. - I precised the reset voltage in the method section. Fig 1, legend: It is not explained how reference 5 is related to the figure. - This no longer apply in the current version of the manuscript It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. - The cFBP is compact because only four input lines need to be defined all the other remaining 12 can take any other value, we now explain this in the result section The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. - The reduction in driving force would be insufficient in an isopotential neuron as it would affect the independence of g_1 and g_2. I now further underline this point in the discussion. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. - I agree the experiment would only demonstrate that granule cells are capable of linearly inseparable computation. However, given their isopotential structure it is unlikely that saturation is due to a localized reduction in driving force. This point is now further emphasised in the discussion. Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - I proposed glutamate spillover as a candidate where nearby synapses interact. However, its effect on firing was never investigated to our knowledge. This work thus make a strong experimental prediction and give a computational role for glutamate spillover. I added two references to further support this argument. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. - I renamed our model the Saturating Integrate and Fire, this model account for a neuron where synapses target distinct points where they interact. I now discuss this statement extensively in our conclusion. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. - I precised that ref.6 concerns pyramidal neurons only and not granule cells. I also insist in the introduction on the fact that granule cells are isopotential structure impossibl to decompose into subunits. In Eq. 1. I_s is input current and not input conductance - I corrected the mistake I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. - I now underline this crucial difference and discuss it in our conclusion Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. - I redraw the panel B and C of this figure to make it clearer. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. - I now explicit this point in the figure legend. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. - Spikes arised when we reached -62mV Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. - I precised the reset voltage in the method section. Fig 1, legend: It is not explained how reference 5 is related to the figure. - This no longer apply in the current version of the manuscript It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. - The cFBP is compact because only four input lines need to be defined all the other remaining 12 can take any other value, we now explain this in the result section The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. - The reduction in driving force would be insufficient in an isopotential neuron as it would affect the independence of g_1 and g_2. I now further underline this point in the discussion. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. - I agree the experiment would only demonstrate that granule cells are capable of linearly inseparable computation. However, given their isopotential structure it is unlikely that saturation is due to a localized reduction in driving force. This point is now further emphasised in the discussion. Competing Interests: No Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Papoutsi A and Chavlis S. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.77421.r94490 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v2#referee-response-94490 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Oct 2021 Athanasia Papoutsi , Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Greece Spyridon Chavlis , Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology, Hellas, Heraklion, Greece Approved VIEWS 0 https://doi.org/10.5256/f1000research.77421.r94490 We appreciate the clarifications/corrections made by the author and we support this work for indexing. We have some minor comments that do not change the impact of the work. Page 5: “You can observe on ... Continue reading READ ALL We appreciate the clarifications/corrections made by the author and we support this work for indexing. We have some minor comments that do not change the impact of the work. Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. Figure Legend: “In the clustered case (grey), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS. Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. Definition 2: the number of variables denoted with n should be in italic for consistency. Competing Interests: No competing interests were disclosed. Reviewer Expertise: computational neuroscience; dendritic computations We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Papoutsi A and Chavlis S. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.77421.r94490 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v2#referee-response-94490 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Feb 2022 Romain Cazé , CNRS UMR 8520, France 03 Feb 2022 Author Response We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on ... Continue reading We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on a strong prediction/hypothesis: that multiple presynaptic neurons target the same postsynaptic receptor, which gives a new role for glutamate spillover. We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on a strong prediction/hypothesis: that multiple presynaptic neurons target the same postsynaptic receptor, which gives a new role for glutamate spillover. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 13 Jun 2022 Romain Cazé , CNRS UMR 8520, France 13 Jun 2022 Author Response Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand ... Continue reading Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Definition 2: the number of variables de noted with n should be in italic for consistency. - I corrected these mistakes which were introduced during the editorial process Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. - I worked on th e figure so panel B and C do not conflict we each other anymore Figure Legend: “In the clustered case (grey ), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). - The panel has now changed and we rewrote the legend. Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS . - I updated the git repo and corrected the text to solve this mismatch Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. - I followed the reviewer's suggestion. Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Definition 2: the number of variables de noted with n should be in italic for consistency. - I corrected these mistakes which were introduced during the editorial process Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. - I worked on th e figure so panel B and C do not conflict we each other anymore Figure Legend: “In the clustered case (grey ), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). - The panel has now changed and we rewrote the legend. Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS . - I updated the git repo and corrected the text to solve this mismatch Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. - I followed the reviewer's suggestion. Competing Interests: No Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Feb 2022 Romain Cazé , CNRS UMR 8520, France 03 Feb 2022 Author Response We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on ... Continue reading We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on a strong prediction/hypothesis: that multiple presynaptic neurons target the same postsynaptic receptor, which gives a new role for glutamate spillover. We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on a strong prediction/hypothesis: that multiple presynaptic neurons target the same postsynaptic receptor, which gives a new role for glutamate spillover. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 13 Jun 2022 Romain Cazé , CNRS UMR 8520, France 13 Jun 2022 Author Response Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand ... Continue reading Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Definition 2: the number of variables de noted with n should be in italic for consistency. - I corrected these mistakes which were introduced during the editorial process Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. - I worked on th e figure so panel B and C do not conflict we each other anymore Figure Legend: “In the clustered case (grey ), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). - The panel has now changed and we rewrote the legend. Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS . - I updated the git repo and corrected the text to solve this mismatch Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. - I followed the reviewer's suggestion. Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Definition 2: the number of variables de noted with n should be in italic for consistency. - I corrected these mistakes which were introduced during the editorial process Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. - I worked on th e figure so panel B and C do not conflict we each other anymore Figure Legend: “In the clustered case (grey ), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). - The panel has now changed and we rewrote the legend. Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS . - I updated the git repo and corrected the text to solve this mismatch Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. - I followed the reviewer's suggestion. Competing Interests: No Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 06 Jul 2021 Views 0 Cite How to cite this report: Papoutsi A. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.57398.r89096 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v1#referee-response-89096 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Jul 2021 Athanasia Papoutsi , Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Greece Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.57398.r89096 This Brief Report shows at a conceptual level that electrically compact neurons can solve non-linearly separable computations of four or more inputs. This is a result of the saturating responses to ‘clustered’ input at the dendritic level (simulating mainly the ... Continue reading READ ALL This Brief Report shows at a conceptual level that electrically compact neurons can solve non-linearly separable computations of four or more inputs. This is a result of the saturating responses to ‘clustered’ input at the dendritic level (simulating mainly the reduction of the driving force in the dendrites), and increased response to ‘scattered’ input at the somatic level. This study expands on previous work of the author and others and adds on the range of computations neurons can perform with their dendrites. We have two major concerns that limit the clarity of this work: Figure 1B and 3rd paragraph on page 5: The x-axis label states ‘Time’, and the relevant text states that “the membrane voltage takes more time to reach threshold in the clustered case (total g = 10pS) than in the scattered case (total g = 20pS)”. Given that this work is based on an arbitrary thresholding of the output frequency, it is not obvious where time is involved and its meaning in the x-axis. In the provided code on GitHub (line 78 in the code), the ceiling of the second dendrite (i.e., syn2 in the code) is set to 0.5 and not to 0.1. Please clarify the value used. If those different saturating thresholds were indeed used, this should be explicitly stated and reasoned in the main text. Minor comments (not in order of importance nor appearance in the manuscript): For clarity, specify that granule cells refer to the cerebellum (and not the hippocampus). Correct R units to be MΩ (not mΩ). Figure 1 legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.” Change the second > to <. Page 4: “This computation that we call the the compact feature binding problem (cFBP) is linearly inseparable.” Delete the second ‘the’. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: computational neuroscience; dendritic computations I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Papoutsi A. Reviewer Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.57398.r89096 ) The direct URL for this report is: https://f1000research.com/articles/10-539/v1#referee-response-89096 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 06 Jul 2021 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 3 (revision) 08 Jun 22 read Version 2 (revision) 16 Sep 21 read read Version 1 06 Jul 21 read Athanasia Papoutsi , Foundation for Research & Technology - Hellas, Heraklion, Greece Spyridon Chavlis , Foundation for Research & Technology, Hellas, Greece Balazs B Ujfalussy , Institute of Experimental Medicine, Budapest, Hungary Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2022 Ujfalussy B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 Jun 2022 | for Version 3 Balazs B Ujfalussy , Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary 0 Views copyright © 2022 Ujfalussy B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In general I accept the author's response to my comments. I have only 2 minor comments, that I list below. The authors renamed the model the Saturating Integrate and Fire, but the Caption of Fig 1 starts with "Dendrited IF". I think this should be corrected for making it consistent with the rest of the paper. In Eq. 2 g_D_1(t) has explicit temporal dependence, g_D_2 does not. Is there any reason for this or it is a simple typo? Also, I would consider denoting the two groups with roman numbers (I and II) of letters (a and b), since arabic numbers are for the inputs (1-4) which can be confusing. Competing Interests No competing interests were disclosed. Reviewer Expertise Computational Neuroscience I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Ujfalussy BB. Peer Review Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.134167.r140000) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-539/v3#referee-response-140000 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2022 Ujfalussy B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 15 Feb 2022 | for Version 2 Balazs B Ujfalussy , Laboratory of Biological Computation, Institute of Experimental Medicine, Budapest, Hungary 0 Views copyright © 2022 Ujfalussy B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In this report the author extends his previous work (ref 3.) demonstrating that simple neurons with two saturating nonlinearities can implement certain non-trivial computational problems, i.e., the feature binding problem (FBP). The novelty of the current implementation is that it places the nonlinearity to the synaptic conductance term in the input instead of to the reduction of the synaptic driving force. This way the FBP can be implemented with electrically compact neurons without the need for independent electrical subunits. I have two main concerns: Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. In Eq. 1. I_s is input current and not input conductance I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. Fig 1, legend: It is not explained how reference 5 is related to the figure. It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Computational Neuroscience I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 13 Jun 2022 Romain Cazé, CNRS UMR 8520, France Although the idea that nonlinear integration between different input streams could be implemented at the synaptic conductance level, actual experimental data supporting this hypothesis is not cited. - I proposed glutamate spillover as a candidate where nearby synapses interact. However, its effect on firing was never investigated to our knowledge. This work thus make a strong experimental prediction and give a computational role for glutamate spillover. I added two references to further support this argument. The authors introduce the Dendritic Integrate and Fire model as a variant of the Leaky Integrate and Fire harboring multiple groups of interacting synaptic conductance. Since the model does not assume that the sites target physically separate dendritic compartments, I found the name potentially misleading. - I renamed our model the Saturating Integrate and Fire, this model account for a neuron where synapses target distinct points where they interact. I now discuss this statement extensively in our conclusion. Specific comments: The introduction states that cerebellar granule cells can be decomposed into multiple independent subunits, but the reference cited [ref. 8] does not directly imply this. - I precised that ref.6 concerns pyramidal neurons only and not granule cells. I also insist in the introduction on the fact that granule cells are isopotential structure impossibl to decompose into subunits. In Eq. 1. I_s is input current and not input conductance - I corrected the mistake I suggest to highlight that g in Eq. 2. denotes the total synaptic conductance associated with a group of input synapses, which is quite unusual assumption for most modelers. - I now underline this crucial difference and discuss it in our conclusion Fig. 1: in panel B grey and black denote the DIF and LIF models. In panel C the same colors denote clustered versus scattered configurations, according to the legend. - I redraw the panel B and C of this figure to make it clearer. Fig 1C: It is unclear what were the inputs used here: As far as I understand, there are 4 spike trains, 2 of which the input frequency F > 50 Hz and 2 with F < 50 Hz. What are the actual frequencies? Are these Poisson trains? Based on the response, I see a strong 60 Hz drive, but I don't see any other periodicity in the response. I would consider showing the timing of the inputs with green and blue ticks above the response. - I now explicit this point in the figure legend. Fig. 1C: It is hard to see the spikes on the response - I would consider showing a larger y-axis range. - Spikes arised when we reached -62mV Fig. 1C: It seems to me that the membrane potential is reset after each spike, but details of this reset are missing. - I precised the reset voltage in the method section. Fig 1, legend: It is not explained how reference 5 is related to the figure. - This no longer apply in the current version of the manuscript It is not clear why cFBP is compact. It is an n=4-dimensional problem, so it is defined by its 2^4=16 input-output pairs. Even if we restrict ourselves to the mappings with exactly 2 of the inputs being active, there are 6 of such pairs. I understand that it remains linearly non-separable no matter how we define the remaining two mappings, but the definition ('compact because they have four input/output lines') still feels somewhat vague and arbitrary. - The cFBP is compact because only four input lines need to be defined all the other remaining 12 can take any other value, we now explain this in the result section The statement 'a reduction in driving force does not generate sublinear summation in a DIF' is false. Reduction of driving force would generate sublinear summation even in a DIF. What the author might want to say is that in this particular example sublinear integration was not associated with reduction of driving force. - The reduction in driving force would be insufficient in an isopotential neuron as it would affect the independence of g_1 and g_2. I now further underline this point in the discussion. Last paragraph: It is unclear how the proposed experiment would test whether the granule cells implementing cFBP use saturating input conductance or driving force reduction as a biophysical mechanism to solve the FBP. - I agree the experiment would only demonstrate that granule cells are capable of linearly inseparable computation. However, given their isopotential structure it is unlikely that saturation is due to a localized reduction in driving force. This point is now further emphasised in the discussion. View more View less Competing Interests No reply Respond Report a concern Ujfalussy BB. Peer Review Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.77421.r121426) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-539/v2#referee-response-121426 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2021 Papoutsi A et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Oct 2021 | for Version 2 Athanasia Papoutsi , Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Greece Spyridon Chavlis , Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology, Hellas, Heraklion, Greece 0 Views copyright © 2021 Papoutsi A et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions We appreciate the clarifications/corrections made by the author and we support this work for indexing. We have some minor comments that do not change the impact of the work. Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. Figure Legend: “In the clustered case (grey), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS. Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. Definition 2: the number of variables denoted with n should be in italic for consistency. Competing Interests No competing interests were disclosed. Reviewer Expertise computational neuroscience; dendritic computations We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (2) Author Response 03 Feb 2022 Romain Cazé, CNRS UMR 8520, France We will respond thoroughly to these minor comments as we receive new review reports. We would like to direct your attention to an important point. This work is based on a strong prediction/hypothesis: that multiple presynaptic neurons target the same postsynaptic receptor, which gives a new role for glutamate spillover. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 13 Jun 2022 Romain Cazé, CNRS UMR 8520, France Page 5: “You can observe on Figure 1 that locally bounding g enables implement the of cFBP”. Consider removing the ‘of’ preposition. Figure Legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.”, please change the second ">" to "<". Definition 2: the number of variables de noted with n should be in italic for consistency. - I corrected these mistakes which were introduced during the editorial process Figure 1B and C: In Figure 1B, the color scheme denotes the LIF vs. DIF models. The same color scheme is used in panel C to denote the scattered vs. clustered case. As it stands now, is confusing. Consider changing the line style on panel C. - I worked on th e figure so panel B and C do not conflict we each other anymore Figure Legend: “In the clustered case (grey ), the neuron reach spike threshold three times whereas it reaches spike thresold seven times in the scattered case (black).” Please correct “thresold” to “threshold”. Also, from the figure, in the in the cluster case it reaches the spike threshold two times (not three). - The panel has now changed and we rewrote the legend. Please update the Github link to point to the revised code. There is a mismatch with code and text: code lines 71 and 77: the g is bounded between 0 and 0.1 nS (i.e., 100pS), while in the text referred to as 10pS . - I updated the git repo and corrected the text to solve this mismatch Figure 1 Legend: We suggest adding the respective episodes of the truth table in Table 1. - I followed the reviewer's suggestion. View more View less Competing Interests No reply Respond Report a concern Papoutsi A and Chavlis S. Peer Review Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.77421.r94490) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-539/v2#referee-response-94490 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2021 Papoutsi A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Jul 2021 | for Version 1 Athanasia Papoutsi , Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Greece 0 Views copyright © 2021 Papoutsi A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This Brief Report shows at a conceptual level that electrically compact neurons can solve non-linearly separable computations of four or more inputs. This is a result of the saturating responses to ‘clustered’ input at the dendritic level (simulating mainly the reduction of the driving force in the dendrites), and increased response to ‘scattered’ input at the somatic level. This study expands on previous work of the author and others and adds on the range of computations neurons can perform with their dendrites. We have two major concerns that limit the clarity of this work: Figure 1B and 3rd paragraph on page 5: The x-axis label states ‘Time’, and the relevant text states that “the membrane voltage takes more time to reach threshold in the clustered case (total g = 10pS) than in the scattered case (total g = 20pS)”. Given that this work is based on an arbitrary thresholding of the output frequency, it is not obvious where time is involved and its meaning in the x-axis. In the provided code on GitHub (line 78 in the code), the ceiling of the second dendrite (i.e., syn2 in the code) is set to 0.5 and not to 0.1. Please clarify the value used. If those different saturating thresholds were indeed used, this should be explicitly stated and reasoned in the main text. Minor comments (not in order of importance nor appearance in the manuscript): For clarity, specify that granule cells refer to the cerebellum (and not the hippocampus). Correct R units to be MΩ (not mΩ). Figure 1 legend: “filled circles stand for a >50 Hz input spike train while empty circles stand for >50 Hz input spike train.” Change the second > to <. Page 4: “This computation that we call the the compact feature binding problem (cFBP) is linearly inseparable.” Delete the second ‘the’. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise computational neuroscience; dendritic computations I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Papoutsi A. Peer Review Report For: All neurons can perform linearly non-separable computations [version 3; peer review: 2 approved] . F1000Research 2022, 10 :539 ( https://doi.org/10.5256/f1000research.57398.r89096) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/10-539/v1#referee-response-89096 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "All neurons can perform linearly non-separable...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/10-539/v3" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/10-539/v3&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/10-539/v3" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Cazé RD'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/10-539/v3/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/10-539", templates : { twitter : "All neurons can perform linearly non-separable computations. Cazé RD, published by " + "@F1000Research" + ", https://f1000research.com/articles/10-539/v3" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/53961/134167") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "134167"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "104064": 0, "99781": 0, "99780": 0, "89095": 0, "99783": 0, "89094": 0, "99782": 0, "89097": 0, "89096": 29, "99784": 0, "121420": 0, "121167": 0, "122127": 0, "121166": 0, "121169": 0, "121425": 0, "121168": 0, "121424": 0, "89235": 0, "121427": 0, "122515": 0, "121426": 14, "122514": 0, "122517": 0, "89236": 0, "121428": 0, "122516": 0, "122518": 0, "94490": 20, "139999": 0, "95073": 0, "140000": 9, "95074": 0, "100517": 0, "100516": 0, "102311": 0, "96551": 0, "102312": 0, "95541": 0, "95540": 0, "95543": 0, "95542": 0, "98873": 0, "99769": 0, "99768": 0, "95544": 0, "123706": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "30703d07-8506-4e07-8764-01de2c72ebb3"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });