An appetitive spatial working memory task for mice, using a semi-automated 8-arm radial maze

preprint OA: gold CC-BY-4.0
📄 Open PDF Full text JSON View at publisher
AI-generated summary by claude@2026-07, 2026-07-14

This paper describes a semi-automated 8-arm radial maze task for mice, using sequential forced and free runs with a time delay to assess spatial working memory and achieve high success rates.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

AI-generated deep summary by claude@2026-07, 2026-07-14 · read from full text

The paper describes an appetitive spatial working memory task for mice implemented on a semi-automated 8-arm radial maze, outlining how animals are trained and tested to retrieve rewards based on spatial memory. It presents the behavioral setup and overall method rather than reporting a specific biomedical mechanism, and it emphasizes task operation details consistent with standardized mouse behavioral performance assays. A key limitation is that the provided content is a protocol description, so it does not present experimental results or explicit caveats about efficacy across different strains or study contexts. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract This methods paper details a protocol to test spatial working memory in mice using a semi-automated radial 8-arm maze (RAM). The RAM is a partially enclosed apparatus with 8 horizontal equally spaced arms radiating from a central hub, from which access to each arm can be controlled individually by servo-controlled motorized doors. Animals start in the central hub and are allowed to explore the maze for a food reward at the end of each arm or selected arms. The RAM task was originally designed for rats but we have adapted the protocol for mice – for example, by including more habituation steps. In our protocol, all arms are initially baited with sweetened condensed milk and mice are admitted sequentially into four pseudo-randomly selected arms to collect the rewards (“forced run”) before opening all doors together to allow the mice to run freely and find the remaining four rewards (“free run”). A 5-sec time delay is introduced between the forced and free runs to challenge working memory; an error is recorded if the mouse enters any previously-visited arm during the free run. The task is complete when all rewards are recovered. After 6 days of habituation and 9 days of maze training, male C57BL/6 mice regularly achieve ≥80% success rate, defined as 4/(4+E) where E is the number of errors. This semi-automated task could in principle be combined with in vivo monitoring methods such as electrophysiology or calcium imaging.
Full text 5,736 characters · extracted from preprint-html · click to expand
:root { --font-inter: 'Inter', 'Roboto', sans-serif, Symbol; --font-monospace: monospace; --font-roboto: 'Roboto', sans-serif, Symbol; --font-sans-serif: sans-serif; } html, body, div, span, h1, h2, h3, h4, h5, h6, p, ol, ul, li, table, caption, tbody, tfoot, thead, tr, th, td {margin: 0;padding: 0;border: 0;font-size: 100%;font: inherit;vertical-align: baseline;} html {font-size: 14px;line-height: 1.5;} body {font-size: 14px;line-height: 1.5;font-family: var(--font-Roboto);} ol, ul {list-style: none;} blockquote, q {quotes: none;} blockquote:before, blockquote:after, q:before, q:after {content: '';content: none;} img {max-width: 100%;} table {border-collapse: collapse;border-spacing: 0;} b {font-weight: bold;} a {color: #2c71d6;text-decoration: none;} p {margin: 10px 0 10px 0;} .no-select {-o-user-select: none;-moz-user-select: none;-khtml-user-select: none;-webkit-user-select: none;user-select: none;} * {box-sizing: border-box;} @keyframes blink {0% {opacity: .1;}20% {opacity: 1;}100% {opacity: .1;}} .main-preloader{position:fixed;display: flex;top:0;bottom:0;left:0;right:0;align-items: center;justify-content: center;z-index:200000000;text-align:center;background:#F2F3F6;} .main-preloader.dark{background:#363636;} .main-preloader.hidden {display: none;} .main-preloader .mp-logo{height:100px;width:100px;} An appetitive spatial working memory task for mice, using a semi-automated 8-arm radial maze V.1 {"@context":"https://schema.org","@id":"https://www.protocols.io/#org","@type":"Organization","logo":{"@type":"ImageObject","height":"1550","url":"https://www.protocols.io/img/protocols-og.jpg","width":"1920"},"name":"protocols.io","url":"https://www.protocols.io/"} {"@context":"https://schema.org","@id":"https://www.protocols.io/#website","@type":"WebSite","name":"protocols.io","publisher":{"@id":"https://www.protocols.io/#org"},"url":"https://www.protocols.io/"} {"@context":"https://schema.org","@id":"https://www.protocols.io/view/strong-an-appetitive-spatial-working-memory-task-261gerzjdl47/v1#webpage","@type":"WebPage","about":{"@id":"https://www.protocols.io/#org"},"description":"An appetitive spatial working memory task for mice, using a semi-automated 8-arm radial maze\n. This me. Read full protocol, steps, and materials on protocols.io","isPartOf":{"@id":"https://www.protocols.io/#website"},"name":"An appetitive spatial working memory task for mice, using a semi-automated 8-arm radial maze\n V.1","url":"https://www.protocols.io/view/strong-an-appetitive-spatial-working-memory-task-261gerzjdl47/v1"} {"@context":"https://schema.org","@id":"https://www.protocols.io/view/strong-an-appetitive-spatial-working-memory-task-d4dw8s7e#article","@type":"Article","author":{"@type":"Person","affiliation":"Dev","name":"Protocols Importer"},"dateModified":"2023-11-03T14:47:28Z","datePublished":"2023-11-03T14:47:28Z","description":"An appetitive spatial working memory task for mice, using a semi-automated 8-arm radial maze\n. This me. Read full protocol, steps, and materials on protocols.io","headline":"An appetitive spatial working memory task for mice, using a semi-automated 8-arm radial maze\n V.1","image":{"@type":"ImageObject","url":"https://files.protocols.io/webapp/images/q2ckeg/meta/protocolsio.png"},"mainEntityOfPage":{"@id":"https://www.protocols.io/view/strong-an-appetitive-spatial-working-memory-task-d4dw8s7e#webpage"},"publisher":{"@id":"https://www.protocols.io/#org"}} var WebappBundlePath = "https:\/\/files.protocols.io\/webapp\/bundles\/production-292b780\/"; var AMPLITUDE_API_KEY="c250ed1d862cb9c01587e1f779a32c9";var FFMPEG_HOST="https://ffmpeg.protocols.io";var FILES_CDN_URL="https://files.protocols.io";var FILES_S3_BUCKET="protocols-io-files";var FILES_S3_PREFIX="external";var GA_MEASUREMENT_ID="G-33ZGV5CT0B";var GOOGLE_CLIENT_ID="182746271041-rdtjk0b1h9706r0fgpq1o2h55lgbo8un.apps.googleusercontent.com";var HAS_SSR= false ;var HOST_ROLE= 1 ;var HOST_ROLES={"HOST_DEV":2,"HOST_LOCAL":3,"HOST_PROD":1};var IMAGE_BASE_PATH="https://files.protocols.io/webapp/images/q2ckeg/";var NATS_SERVER="wss://ws.protocols.io";var ORCID_CLIENT_ID="APP-7405TP4958GFSZXV";var ORCID_URL="https://orcid.org/oauth/authorize";var RECAPTCHA_SITE_ID="6LeMF6YUAAAAAOYzOqfc9Ml3PdTgIzO1oDdIwCYc";var SITE_URL="https://www.protocols.io";var SPRINGERNATURE_CLIENT_ID="protocolsio";var SPRINGERNATURE_REDIRECT="https://www.protocols.io/api/v1/auth/springernature/authcallback";var SPRINGERNATURE_URL="https://idp.springernature.com";var STRIPE_PUB_KEY="pk_live_51PfMxdRshFFmaQRVGeDsSX7NFFLMkMuqr17SxP6JcJuieglmQYHPgM7cz0XdO91IEVP9k4E4MC7PZB2ATyuuaOy800ivYpRURi";var UIPE="ccW9Kn73Dw3C4V1lovN9duN1d5BCqNRXHRUQiYoxsGA=";var VPC_ALLOW_REGENTS_LINKING= false ;var VPC_DISABLED_INVITE_COLLEAGUES= false ;var VPC_DISABLED_PUBLISHING= false ;var VPC_DISABLED_SIGN_UP= false ;var VPC_DISABLED_WORKSPACE_CREATION= false ;var VPC_EXTERNAL_STORAGES= null ;var VPC_FORWARDED_VIEW_URL_PREFIX="";var VPC_MAIN_CLOUD_URL="https://www.protocols.io";var VPC_REQUIRE_SIGN_IN= false ;var VPC_USE_MAIN_GLOBAL_SEARCH= false ;var WEBAPP_VERSION="production-292b780";var YJS_WEBSOCKET_URL="wss://www.protocols.io";var __stripeGlobal= null ;var cookie_session_guid="06D0CD777F5E11F1AE980A58A9FEAC02";var gosessid="06D0CBC67F5E11F1AE980A58A9FEAC02";var is_premium= false ;var loadedAmplitude= false ;var loadedGA= false ;var logged_in= false ;var session_guid="";var show_loader= true ;var user_theme="default"; var is_grecaptcha_loaded = false; if (!Element.prototype.matches) Element.prototype.matches = Element.prototype.msMatchesSelector; if (!Element.prototype.closest) Element.prototype.closest = function (selector) {var el = this; while (el) {if (el.matches(selector)) return el; el = el.parentElement;}};

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. The paper's references may be in our DB but unresolved to ``paper_id`` (resolution happens at ingest when the cited DOI matches a row we already have). Run the cross-source citation reconcile pass to retry.

Source provenance

europepmc
last seen: 2026-05-19T01:45:01.086888+00:00
unpaywall
last seen: 2026-05-21T05:10:58.409756+00:00
License: CC-BY-4.0