A universal hippocampal memory code across animals and environments

Conserved hippocampal population geometry supports task generalization | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Conserved hippocampal population geometry supports task generalization View ORCID Profile Hannah S Wirtshafter , View ORCID Profile Sara A Solla , View ORCID Profile John F Disterhoft doi: https://doi.org/10.1101/2024.10.24.620127 Hannah S Wirtshafter 1 Department of Neuroscience, Northwestern University Feinberg School of Medicine , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hannah S Wirtshafter For correspondence: hsw{at}northwestern.edu Sara A Solla 1 Department of Neuroscience, Northwestern University Feinberg School of Medicine , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sara A Solla John F Disterhoft 1 Department of Neuroscience, Northwestern University Feinberg School of Medicine , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John F Disterhoft Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract How learning generalizes across contexts is a fundamental question in neuroscience, as successful behavior often requires transferring acquired knowledge to new environments. Conditioning tasks provide a clear example of such generalization, with learned responses rapidly expressed across distinct spatial contexts. A central challenge in understanding the neural basis of this ability is determining how the hippocampus represents task-related information across environments, given that its spatial representations remap with context. Here, we used calcium imaging to record hippocampal population activity as rats performed a conditioning task across multiple spatial contexts. To characterize task-related population structure, we applied dimensionality reduction and alignment methods to construct low-dimensional manifolds of hippocampal activity. We found that task-related population activity occupied a stable geometric structure across contexts, despite pronounced remapping of spatial representations. Strikingly, this task-related geometry was conserved not only across contexts within individual animals but also across animals, revealing a shared organization of task representations in the hippocampus. These findings provide a population-level account of how task-related information is preserved across changing spatial environments and suggest that hippocampal task representations follow shared population-level geometric organization across individuals. Introduction How can learning generalize across contexts while remaining sensitive to contextual differences? This question is fundamental to both neuroscience and philosophy 1 – 4 . Deficits in generalization or inappropriate generalization are hallmarks of many disorders, including autism 5 , 6 , schizophrenia 7 , 8 , and post-traumatic stress disorder 9 , 10 . In spite of their importance, many questions related to generalization remain to be answered. The hippocampus (HPC) is important for learning, memory, and navigation, and damage to this region can disrupt contextual learning 11 – 17 . Many aspects of context, including an animal’s spatial location and the presence of local and distal cues, can be represented by ‘place cells’ in the HPC 18 – 22 . The activity of many HPC cells therefore changes drastically in different environments (i.e. place cells remap), even when a task can be generalized across these different contexts 23 – 25 . A major open question is how representations of task-relevant stimuli, which are also found in the HPC 26 – 31 , are organized across environments in which spatial representations remap. A further question is whether different animals solve this problem using the same, or similar, neural strategies. Until recently, the neural mechanisms behind contextual learning have been challenging to investigate due to the need for tracking large numbers of cells across various environments and learning stages— tasks which were unachievable prior to the development of calcium imaging 32 – 37 . Calcium imaging enables population-level analyses of hippocampal activity across environments and learning stages, allowing direct comparisons of neural representations beyond single-cell tuning. This study addresses two population-level questions about hippocampal representations during learning. First, how is task-related information structured across neural populations when spatial representations reorganize across contexts 24 , 25 , 38 , 39 ? Second, to what extent is this population structure conserved across individuals? If task-related hippocampal activity follows shared organizational principles across animals, this would suggest that learning is supported by common population-level frameworks rather than idiosyncratic neural solutions. To answer these questions, we trained animals on an HPC-dependent conditioning task which is rapidly generalized between spatial contexts 40 , 41 . We examined the same task in disparate environments and quantified how population-level representations of spatial location changed while task-related population activity was preserved. We found that task representations were maintained as animals generalized learning across contexts, despite pronounced changes in spatial representations. Strikingly, task-related population structure was also conserved across animals. Together, these results demonstrate that hippocampal task representations exhibit a stable population-level organization that persists across spatial contexts and is conserved across individuals. This conserved structure provides a framework for understanding how learning generalizes across environments at the level of neural populations. Results We trained five freely moving rats in a conditioning task in one of two distinct environments, labeled A and B; the rats had been previously familiarized with the environment by the time the training began ( Fig. 1a , Fig. S1 ). Environment A was an unscented rectangular enclosure with wire floor and walls, and white lighting; environment B was a scented ovular enclosure with white solid floor and walls, and red lighting ( Fig. 1b ). Both environments were located at the same spot in the room relative to external cues (see Methods); animals could see external cues out of the top of both environments, as well as out of the sides of environment A (animals also reared often, allowing them to see out of the sides of environment B). During training sessions, we recorded cellular activity in the hippocampal CA1 region via miniscopes, using GCaMP8m for calcium imaging (CaImg). To perform data analysis, we used both calcium events and calcium traces (see Methods), as indicated when applicable. Download figure Open in new tab Fig 1. Conditioning is maintained while place cells remap between environment A and environment B. a. Animals explored environment A for one session before undergoing eyeblink conditioning until they reached the learning criterion. Criterion was defined as achieving 70% conditioned responses (CRs) in 50 trials across three consecutive training sessions (A(n-2), A(n-1), and A(n)) or averaging over 70% CRs across the previous four training sessions. After meeting the criterion in environment A, animals were allowed one session of exploration in environment B, followed by two test sessions of trace eyeblink conditioning (B(1) and B(2)) in environment B. b. (Top) Schematics of environments A and B. Environment A is a rectangular enclosure with wire walls, floor, and ceiling, lit with white light, and unscented. Environment B is oval-shaped with solid white floors and walls, without a ceiling, lit with red light, and scented with clove oil. Both environments provided distal cues visible from the top and sides. (Bottom) Animal trajectories in environments A and B during a single session, extracted using DeepLabCut. c. Trace eyeblink conditioning (tEBC) paradigm. A 250 ms tone (conditioned stimulus, CS) was followed by a 500 ms trace interval, then a 100 ms eyelid shock (unconditioned stimulus, US). Eyelid activity was recorded using an EMG electrode implanted above the eye. Untrained animals only blinked in response to the US (unconditioned response, UR); trained animals began blinking during the trace interval after the CS and before the US (conditioned response, CR). d. Performance of animals (n=5) in the tEBC task. Animals learned tEBC while freely moving in environment A and successfully transferred this learning to environment B. The dotted line indicates the performance criterion. No significant difference was found between performance in the criterion sessions in environment A (mean 74.75 ± 6.49%) and the test sessions in environment B (mean 77.70 ± 11.68%; two-tailed t-test, t(24) = - 0.83, p > 0.05). Error bars represent standard error. e. Example activity map of a specific single cell in environments A and B. Yellow indicates the highest firing rates. This cell exhibited remapping between environments, showing different place fields relative to external cues in the two contexts. f. Distribution of distances between place field centers (determined by the highest calcium event rate), comparing sessions A(n) and A(n-1) versus sessions A(n) and B(1). Place field centers shifted significantly more when the animal was moved to environment B compared to shifts between two consecutive sessions in environment A (Wilcoxon rank sum test, p = 0.002; two-sided t-test, t(1430) = -2.5, p = 0.01; two-sample Kolmogorov-Smirnov test, p = 3.6 × 10). The brown shaded area represents the overlap between distance histograms. g. Population vector correlation (PVC) based on calcium events for sessions A(n-1), A(n), and B(1). A significant positive correlation was found between sessions A(n-1) and A(n) for cells present in both sessions (p = 0.0023, r = 0.11). In contrast, there was no significant correlation between sessions A(n) and B(1) for shared cells (p > 0.05, r = - 0.04). These findings suggest that calcium event patterns are significantly similar between sessions A(n-1) and A(n) but not between session A(n) and B(1). Dashed lines represent lines of best fit. (a.u. = arbitrary units). Animals easily transfer a conditioning task across environments despite place cell remapping During the initial phase of our study, freely-moving rats ( Fig. 1b ) underwent training for trace eyeblink conditioning (tEBC) ( Fig. S2 ), a hippocampus-dependent classical conditioning task that serves as a robust model for associative memory formation 42 – 45 . This paradigm involves presenting a 250ms conditioned stimulus (CS, in the form of a tone) followed by a 500ms trace interval, followed by the 100ms presentation of an unconditioned stimulus (US, an eyelid shock) ( Fig. 1c ). Both shock and blinking were recorded with wires inserted into the muscle of the eyelid (see Methods). As rats were trained, they exhibited a conditioned blink (CR) to the tone. Animals were considered to have learned the task after reaching criterion (70% CRs in 50 trials) on three consecutive training sessions (termed ‘criterion sessions’) or when the previous four training sessions averaged over 70% (in this instance, only the final three of those sessions were considered ‘criterion sessions’) ( Fig. 1d ). There was substantial variability in the number of sessions it took to learn the task, for an average of 20±4.2 training sessions (note: number of sessions always includes criterion sessions). The two rats that learned the fastest reached criterion in 14 sessions, and the rat that learned the slowest reached criterion after 24 sessions ( Fig. S2 ). After reaching criterion, the rats were introduced to environment B, where their ability to perform tEBC was assessed over a two-day period (one session per day). Comparative analysis revealed no significant difference in performance (measured in % CRs) between the criterion sessions in environment A and the testing phase in environment B (mean in environment A criterion sessions was 74.75±6.49, mean in environment B test sessions was 77.70±11.68, two tailed t-test(24)=-0.83, p>0.05) ( Fig. 1d ), indicating the successful transfer of tEBC learning to a new environment. Consistent with place cell remapping, individual hippocampal neurons exhibited distinct spatial representations in environments A and B ( Fig. 1e ). Putative place field centers shifted significantly more between environments than between consecutive sessions within the same environment ( Fig. 1f ), indicating a loss of spatial correspondence across contexts (double-sided t-test, t(1430) = −2.5, p < 0.01). At the population level, population vector correlations (PVC37,45,46; see Methods) revealed significant similarity between consecutive sessions within environment A but no significant similarity between sessions in environments A and B ( Fig. 1g ), confirming robust remapping of spatial representations. Additional analyses using shuffled controls and population embedding methods yielded consistent results ( Fig. S3 – S4 ). Conditioning task representations are consistent across environments We then asked whether task-related information could be decoded across environments at the population level. Using CEBRA models trained on calcium imaging data labeled with CS and US periods from day A(n) (termed CSUS2 for the division of the task into 2 labeled epochs), we decoded task epochs for day A(n-1) as well as in environment B. Models trained only on data from environment A reliably decoded CS and US periods in both environments, significantly outperforming shuffled controls ( Fig. 2a–c , all double sided t-tests p<0.001), demonstrating that task-related population activity generalized across contexts. Download figure Open in new tab Fig. 2. A model trained in environment A can decode CS and US periods in both environment A and B at above chance levels, including fine-grained temporal decoding. a. A CEBRA model was trained using calcium imaging data and time-stamped CS/US periods from environment A, using only cells that were recorded in both environments A and B. The model was used to decode whether the animal was in a CS or US period in another session in environment A; all models successfully decoded these periods compared to shuffled data (all double-sided t-tests: Rat 1: t(998) = 6.7, p = 2.5 × 10 -11 ; Rat 2: t(998) = 16.1, p = 7.4 × 10 -52 ; Rat 3: t(998) = 83.5, p = 0; Rat 4: t(998) = 80.1, p = 0; Rat 5: t(998) = 61.0, p = 0). Bars indicate standard error; significance denoted as ***p < 10 -10 . b. The same models from panel a were now applied to environment B; they significantly outperformed chance level (all double-sided t-tests: Rat 1: t(998) = 10.4, p = 2.6 × 10 -24 ; Rat 2: t(998) = 2.7, p = 7.6 × 10 -3 ; Rat 3: t(998) = 75.3, p = 0; Rat 4: t(998) = 63.7, p = 0; Rat 5: t(998) = 106.3, p = 0). Error bars represent standard error, *p < 10 -3 , ***p < 10 -10 . c. (Top row) A CEBRA model trained on CS/US periods, divided into two time bins (data from Rat 4). For the top first three graphs, the model was trained using position and calcium trace data from session A(n), using cells present in both A(n) and A(n-1). First graph: The trained model applied to training data from session A(n). Second graph: The model applied to decode CS/US periods from held-out data (25%) from session A(n). Third graph: The model applied to session A(n-1). For the top last three graphs, the model was trained using position and calcium trace data from session A(n), using cells present in both A(n) and B(1). Fourth graph: The trained model applied to training data from session A(n). Fifth graph: The model applied to decode CS/US periods from held-out data (25%) from session A(n). Sixth graph: The model applied to session B(1). (Bottom row) The same model trained on shuffled data. The model trained on data from session A(n) significantly outperformed the model trained on shuffled data in decoding both session A(n-1) and session B(1) (double-sided t-tests, t(998) = 83.5, p = 0, and t(998) = 75.3, p = 0, respectively). d. A CEBRA model was trained on data from environment A to decode temporal order within the CS, trace, and US periods split into five divisions, applied to an alternate session in environment A (session A(n-1)). The model significantly outperformed chance (all double-sided t-tests for accuracy: Rat 1: t(998) = 41.5, p = 3.3 × 10 -220 ; Rat 2: t(998) = 28.7, p = 7.8 × 10 -133 ; Rat 3: t(998) = 122.6, p = 0; Rat 4: t(998) = 118.5, p = 0; Rat 5: t(998) = 62.6, p = 0). Bars indicate standard error; significance denoted as ***p < 10 -10 . e. The same five models from panel d were now applied to environment B and outperformed models trained on shuffled data in decoding the temporal aspects of the CS/US periods, indicating that fine-grained temporal encoding is stable across environments (all double-sided t-tests for accuracy: Rat 1: t(998) = 55.1, p = 4.9 × 10 -305 ; Rat 2: t(998) = 9.3, p = 1.1 × 10 -19 ; Rat 3: t(998) = 71.4, p = 0; Rat 4: t(998) = 62.6, p = 0; Rat 5: t(998) = 106.2, p = 0). Bars indicate standard error; significance denoted as ***p < 10 -10 . f. Same analysis as in panel c, but for a CEBRA model trained on CS/US periods split into five time bins. Top: The five divisions of the conditioning period are shown. Data from Rat 3 show that the model trained on session A(n) outperformed models trained on shuffled data when decoding both session A(n-1) and session B(1) (double-sided t-tests, t(998) = 88.9, p = 0, and t(998) = 71.4, p = 0, respectively). g. Confusion matrices displaying CEBRA decoding of five CS/US time bins, as shown in panel f. Top row: Models trained on data from session A(n). Bottom row: Models trained on shuffled data. Darker colors along the diagonal indicate higher model accuracy. h. Model accuracy for decoding sessions A(n-1) and B(1) using a model trained on data from session A(n) with CSUS2 and CSUS5 divisions. For both CSUS2 and CSUS5, a model trained in session A(n) decoded session B(1) with accuracy similar to that achieved when decoding session A(n-1) (CSUS2: double-sided t-test, t(8) = -0.13, p > 0.05; CSUS5: t(8) = 0.32, p > 0.05). Bars represent standard error To determine whether finer temporal structure within the conditioning period was also preserved, we trained models to decode the temporal sequence of task epochs spanning CS presentation, the trace interval, and US delivery (CSUS5). Models trained on session A(n) accurately decoded both session A(n−1) and session B(1), indicating that detailed temporal aspects of task encoding generalized across environments ( Fig. 2d–g , all double sided t-tests p0.05) ( Fig. 2h ). Finally, to directly test whether task representations shared a conserved population geometry across environments, we compared the embedding structure of task-related activity using CEBRA. Embedding geometries for task epochs were highly similar across sessions in environments A and B and were significantly more similar than shuffled controls. This similarity was robust across multiple embedding dimensionalities and was preserved when task periods were divided into either coarse (CSUS2) or fine (CSUS5) temporal segments ( Fig. 3a–f ). Download figure Open in new tab Fig 3. High consistency in neural representations between environments A and B. a. Consistency scores for each rat, calculated with 2, 3, 5, 7, and 10 latents. Lighter bars represent consistency percentage for actual data, while adjacent darker bars represent consistency for shuffled data. The schematic below the x-axis illustrates the data sets included in each comparison (for position and labelling of data sets see figure 6b). For the CSUS2 division of conditioning periods (split into CS and US components), all five rats show significantly higher consistency between environments A and B in the actual data compared to shuffled data. This consistency remains significant with up to 10 latents. b. Example consistency measurements from Rat 5 for CSUS2 with 2, 3, 5, 7, and 10 latents. c. Average consistency scores across all rats for CSUS2. The figure legend follows the format of panel a. Bars represent standard error. Consistency scores for actual data versus shuffled data were significantly different across all latent dimensions (all double-sided t-tests: 2 latents, p < 1 × 10 -5 ; 3 latents, p < 1 × 10 -4 ; 5 latents, p < 1 × 10 -5 ; 7 latents, p < 1 × 10 -5 ; 10 latents, p < 1 × 10 -4 ). d. Consistency scores for each rat, as in panel a, but for CSUS5, where the conditioning period is divided into five time bins. All five rats show significantly higher consistency between environments A and B in the actual data compared to shuffled data; this is maintained across latents for up to 10. e. Example consistency measurements from Rat 3 for CSUS5 with 2, 3, 5, 7, and 10 latents. f. Average consistency scores across all rats for CSUS5. The figure legend matches that of panel c. Bars represent standard error. Significant differences between actual and shuffled data were observed for all latent dimensions (all double-sided t-tests: 2 latents, p < 1 × 10 -12 ; 3 latents, p < 1 × 10 -7 ; 5 latents, p < 1 × 10 -6 ; 7 latents, p < 1 × 10 -5 ; 10 latents, p < 1 × 10 -5 ). Together, these results indicate that task-related hippocampal representations maintain a stable population-level geometry across environments, despite changes in spatial representations. Task representations are conserved across animals Given the similarity of task-related representations across environments within individual animals, we next asked whether task representations also exhibit a shared population-level organization across animals. To address this question, we trained animal-specific models based on calcium activity and task structure and quantified the similarity between models derived from different animals. Task-related population representations showed a high degree of similarity across animals compared to shuffled controls. This similarity was evident both for models distinguishing coarse task epochs (CSUS2) and for models capturing finer temporal structure across five task segments (CSUS5) ( Fig. 4a–b ). Download figure Open in new tab Figure 4. High degree of consistency between the conditioning representations across animals a. Consistency across animals for CSUS2 with 2, 3, 5, 7, and 10 latents. The larger graph highlights the bracketed area from the graph with two latents. b. Conditioning period is divided into 2 time bins (CSUS2): the similarity between models across animals is not significantly different from the similarity between models for an individual animal, regardless of the number of latents (2 latents: t(188) = -0.45, p > 0.05; 3 latents: t(188) = -1.57, p > 0.05; 5 latents: t(188) = -0.40, p > 0.05; 7 latents: t(188) = - 0.22, p > 0.05; 10 latents: t(188) = 0.40, p > 0.05). Error bars represent standard error. c. Consistency across animals for CSUS5 with 2, 3, 5, 7, and 10 latents. The larger graph highlights the bracketed area from the graph with two latents. d. Conditioning period is divided into 5 time bins (CSUS5); the similarity across animal models remains comparable to that for within-animal models, regardless of the number of latents (2 latents: t(188) = -0.79, p > 0.05; 3 latents: t(188) = 0.25, p > 0.05; 5 latents: t(188) = -0.42, p > 0.05; 7 latents: t(188) = 0.70, p > 0.05; 10 latents: t(188) = 0.30, p > 0.05). Error bars represent standard error. Notably, the similarity of task-related population geometry across individuals was comparable in magnitude to the similarity observed across sessions within the same animal. In fact, across-animal similarity was not significantly different from within-animal similarity for any tested embedding dimensionality, for either coarse (CSUS2) or fine-grained (CSUS5) task segmentation) ( Fig. 4c–d ) (all paired t-tests, p>0.05). This indicates that task-related population geometry is conserved across animals to a comparable extent as it is across sessions within an individual. Together, these results demonstrate that hippocampal task representations follow a shared population-level organization across animals, consistent with conserved geometric structure rather than idiosyncratic task encoding. Discussion Our results show that hippocampal representations of a conditioning task are organized in a manner that supports generalization across spatial contexts and consistency across individuals. Although spatial representations in CA1 remap robustly between environments, task-related population activity preserves a stable structure across contexts and animals. These findings indicate that task-relevant information is maintained at the level of population organization, providing a mechanism by which learned behaviors can be flexibly expressed despite changes in spatial context. Task abstraction across environments A central finding of this study is that hippocampal representations of a conditioning task remain stable across distinct spatial environments, even as place cell representations reorganize. This dissociation between spatial remapping and task stability suggests that task-related information is not simply inherited from spatial coding, but is supported by an additional representational structure at the population level. Such organization allows the hippocampus to support behavioral generalization when task demands remain constant across environments. These results align with theoretical frameworks proposing that hippocampal representations extend beyond physical space to include abstract task structure and relational information 46 . In such models, spatial and task-related variables are jointly embedded within a higher-dimensional representational space, allowing learned behaviors to be applied in novel environments that share cognitive demands but differ in sensory or contextual features. Our findings provide empirical support for this view by showing that hippocampal population activity preserves task-related structure across environments when task contingencies are unchanged. Importantly, the conditioning task used here was explicitly designed to be identical across contexts, enabling us to isolate generalization of task representations from contextual discrimination. Under these conditions, hippocampal population activity maintained task-related structure across environments, suggesting that the hippocampus can abstract task-relevant information from surrounding sensory details when such abstraction is behaviorally appropriate 47 – 49 , 55 , 56 . Pattern separation and completion reflect task demands The dissociation between spatial remapping and task stability in our data speaks to longstanding debates regarding hippocampal pattern separation and pattern completion 46 – 48 , 50 – 56 . Robust remapping of place cell representations across environments is consistent with pattern separation, supporting the formation of distinct contextual representations and reducing interference between memories. At the same time, the preservation of task-related representations across environments reflects pattern completion, allowing a learned response to be expressed despite changes in contextual cues. Our results suggest that the balance between pattern separation and pattern completion depends on whether task demands change across contexts, rather than on contextual change alone. When task demands are preserved, as in our study, hippocampal representations favor generalization of task-related information. In contrast, studies in which task contingencies differ across environments have reported strong representational divergence, even when behavioral demands appear superficially similar 54 , 57 – 60 . Together, these findings indicate that hippocampal representations flexibly balance separation and completion in a manner that reflects task structure. Population-level organization supports non-spatial representations There remains active debate regarding the extent to which hippocampal pyramidal neurons encode non-spatial information 31 , 61 – 63 . Importantly, the stability of task-related population structure observed here cannot be explained by the engagement of a distinct, task-specific subpopulation. Instead, prior analyses demonstrate substantial overlap between neurons encoding spatial and task-related information 31 , indicating that task generalization emerges from shared population resources rather than segregated cell classes. Specifically, task-related activity formed stable low-dimensional population structures across environments, despite changes in spatial coding. Such population-level organization offers a solution to the problem of mixed selectivity at the single-cell level, allowing different task and contextual variables to be represented along distinct dimensions within a shared population space 64 . Similar organizational principles have been observed in other brain regions, including prefrontal and cingulate cortex 65 – 67 , and in artificial neural networks trained on context-dependent tasks 68 . Our findings extend this population-level framework to hippocampal representations of learned tasks across contexts. Task representations are conserved across animals Beyond stability across environments, we found that task-related population organization was conserved across animals. Notably, the similarity of task-related population geometry across individuals was comparable to the similarity observed across sessions within the same animal. This result suggests that hippocampal task representations follow shared organizational principles across animals, rather than being entirely idiosyncratic to individual experience. Such conservation is striking given the hippocampus’s established role in episodic memory and its sensitivity to individual experience 69 – 73 . Prior studies reporting conserved neural dynamics across animals have largely focused on systems associated with motor control or basic motivational states 74 – 82 , which are often linked to stereotyped behaviors 79 , 81 , 83 , 84 . In contrast, hippocampal-dependent tasks involve flexible cognitive processes shaped by learning and context 85 – 88 . The observation that hippocampal task representations exhibit consistent population-level organization across animals suggests that common representational strategies may underlie flexible cognitive functions. Conclusion Together, our findings indicate that the hippocampus supports behavioral generalization by organizing task-related activity into stable population-level structures that persist across contexts and individuals. This organization allows learned behaviors to be flexibly expressed despite changes in spatial representations, providing a neural basis for cognitive flexibility. Rather than encoding task information solely at the level of individual neurons, the hippocampus appears to rely on population-level geometry to preserve task structure across varying conditions. These results add to a growing body of work emphasizing population-level organization as a fundamental principle of neural computation and suggest that conserved representational geometry may support flexible cognition. By demonstrating how task representations are maintained across environments and animals, this study advances our understanding of how the hippocampus integrates learning, memory, and generalization. Methods LEAD CONTACT AND MATERIALS AVAILABILITY Questions and requests for information should be directed to and will be fulfilled by the Lead Contact, Hannah Wirtshafter ( hsw{at}northwestern.edu ). This study did not generate new unique reagents. The data that support the findings of this study are available from the corresponding author. EXPERIMENTAL MODEL AND SUBJECT DETAILS All procedures were performed within Northwestern Institutional Animal Care and Use Committee and NIH guidelines. Five male Long Evans rats (275–325 g) were sourced from Charles River Laboratories, injected with AAV9-GCaMP8m, implanted with a 2-mm GRIN lens, and trained and tested on eyeblink conditioning in two apparatuses ( Fig. 1 ). Animals were individually housed in an animal facility with a 12/12 h light/dark cycle. METHOD DETAILS GCaMP7c injection, lens implantation, EMG implantation GCaMP8 injection and lens implantation were completed as reported in Wirtshafter and Disterhoft, 2022 and Wirtshafter and Disterhoft, 2023 1 , 2 . Briefly, rats were anesthetized with isoflurane (induction 4%, maintenance 1-2%) and a craniotomy was performed at stereotaxic coordinates Bregma AP −4.00mm, ML 3.00mm. 0.06uL of GCaMP8m (obtained from AddGene, packaged AAV9 of pGP-AAV-syn-jGCaMP8m-WPRE, lot v175525, titer 1.3E+13 GC/mL) was injected over 12 minutes (approximate coordinates Bregma AP −4.00mm, ML 3mm, DV 2.95mm relative to skull); then the syringe was raised 0.2mm and an additional 0.6ul of GCaMP8 was injected. We repeated this process once more and at slightly different coordinates in the craniotomy hole, resulting in 4 total injections. We then aspirated tissue from the craniotomy site using a vacuum pump and 25 gauge needle. Tissue was aspirated up to and including the horizontal striations of the corpus collosum. A 2mm GRIN lens (obtained from Go!Foton, CLH lens, 2.00mm diameter, 0.448 pitch, working distance 0.30mm, 550nm wavelength) was then inserted into the craniotomy hole and cemented in place using dental acrylic. Animals were given buprenorphine (0.05mg/kg) and 20mL saline, taken off anesthesia, and allowed to recover in a clean cage placed upon a heat pad. Six to eight weeks after surgery, animals were again anesthetized with isoflurane and checked for GCaMP expression. If expression was seen, baseplates were attached using UV-curing epoxy and dental acrylic. Electrode implantation to record obicularis oculi electromyographic (EMG) activity occurred in the same surgery as baseplate attachment, as described previously 3 , 4 . Briefly, a connector containing 5 wires was cemented on the front of the animal’s head: 4 wires were implanted directly above the eye in the surrounding muscle (2 for recording, 2 for electrical stimulation). An additional wire was attached to a connector attached to a ground screw located above the cerebellum; this screw was implanted during lens implantation surgery. Behavioral environment and training Two behavioral apparatuses were used in these experiments: Environment A was a 78.7cm x 50.8cm unscented rectangular enclosure with wire floor and walls and white lighting. Environment B was a 50.1cm x 34.9cm scented (with two dabs of clove essential oil on opposite walls) ovular enclosure with white solid floor and walls, and red lighting. Both environments were located at the same spot in the room relative to external cues (see Figures 1b and S1 ). A tether containing a plug to relay the EMG activity and to deliver a shock to the rat’s eye was attached to a the eyeblink connector on the rat’s head. The miniscope was plugged into the cemented baseplate. The miniscope and EMG cords were all attached to a commutator for ease of animal movement. The CS was a 250ms, 85dB free-field tone (5ms rise-fall time). The US was a 100ms shock directed to the left eye. Shock amount varied per session per animal and was calibrated, if needed, at the end of a training session for the next session’s training. Shock level was deemed appropriate when a shock was met with a firm shake of the animal’s head. The trace interval was 500ms and the intertrial interval (ITI) was randomized between 30s and 60s, with a 45s average. EMG signal output was amplified (5000×) and filtered (100 Hz to 5 kHz), then digitized at 3 kHz and stored by computer. A conditioned response (CR) was identified as an increase in integrated EMG activity that exceeded the baseline mean amplitude by more than four standard deviations, sustained for a minimum duration of 15ms. Baseline mean amplitude was calculated during the 500ms preceding CS onset. Additionally, the response had to commence at least 50ms after the conditioned stimulus (CS) onset and before the unconditioned stimulus (US) onset. The animal’s first exposure to each environment was a 38min exploration session, in which the animal was able to freely move and explore the environment without any conditioning ( Figure 1a ). Animals were then trained in one environment per session, with no more than one session per day, and were considered to have learned the task after reaching criterion (70% CRs in 50 trials) on three consecutive training sessions (termed ‘criterion sessions’) or when the previous four training sessions averaged over 70% (in this instance, only the final three of those sessions were considered ‘criterion sessions’). Following the last session in environment A, the animal was given an exploratory session in environment B. The session after that, the animal was tested on eye blink conditioning in environment B, using the same parameters as used in environment A. Calcium imaging Calcium imaging was completed as reported in Wirtshafter and Disterhoft, 2022 and Wirtshafter and Disterhoft, 2023 1 , 2 . Briefly, calcium imaging was done using UCLA V4 Miniscopes 5 , 6 , assembled with two 3mm diameter, 6mm FL achromat lens used in the objective module and one 4mm diameter, 10mm FL achromat lens used in the emission module. QUANTIFICATION AND STATISTICAL ANALYSIS Means are presented as mean+-standard deviation. All analysis code is available at https://github.com/hsw28/ca_imaging and https://github.com/hsw28/Hannahs-CEBRAs . Code to create specific figures is also available at the former github repository. Position and speed analysis Position was sampled by an overhead camera at 30Hz. Position tracking was done post-recording using DeepLabCut 7 . Position was then converted from pixels to cm. Position was smoothed using a Gaussian filter with standard deviation of 2cm. Speed was calculated by taking the hypotenuse of the coordinates one before and after the time of interest. Video pre-processing and cell identification Video pre-processing and cell identification were performed as reported in Wirtshafter and Disterhoft, 2022 and Wirtshafter and Disterhoft, 2023 1 , 2 . In brief, videos were recorded with Miniscope software at 15frames/second. Video processing was done using CIATAH software 8 . Videos were down sampled in space and normalized by subtracting the mean value of each frame from the frame. Each frame was then normalized using a bandpass FFT filter (70-100cycles/pixel) and motion corrected to a using TurboReg 9 . Videos were then converted to relative florescence (dF/F 0 ); F 0 was the mean over the entire video. Cells were automatically identified using CIATAH 8 using CNMF-E 10 . Images were filtered with a gaussian kernel of width 2 pixels and neuron diameter was set at a pixel size of 8. The threshold for merging neurons was set at a calcium trace correlation of 0.65; neurons were merged if their distances were smaller than 4 pixels and they had highly correlated spatial shapes (correlation>0.8) and small temporal correlations (correlation <0.4). In vivo calcium imaging involves detecting changes in intracellular calcium levels, which serve as proxies for neuronal activity. Calcium events refer to transient increases in calcium concentration above a threshold level; these crossings putatively correspond to spikes in neuronal firing. These events typically appear as peaks in the data and indicate an active response from the neuron. Calcium traces are continuous recordings of calcium levels over time. Thus, calcium events highlight specific neuronal activations, while calcium traces provide a full temporal picture of these activations together with baseline activity. All cells identified using CNMF-E were then scored as neurons or not by a human scorer. Scoring was also done within CIATAH software in a Matlab GUI. Scoring was done while visualizing and considering a calcium activity trace, average waveform, a montage of the candidate cell’s Ca2+ events, and a maximum projection of all cells on which the candidate cell was highlighted. The relative fluorescence (ΔF/F 0 ) local maxima of each identified cell were considered calcium event times. Cell cross registration across sessions and within session Validation and registration were completed as documented in Wirtshafter and Disterhoft 2 . Briefly, videos underwent five rounds of registration using Turboreg image rotation 9 with the CIATAH software 8 , 11 . Background noise, axons, and dendrites were removed using an image binarization threshold of 40% of the images’ maximum value. Cells were matched across sessions using a distance threshold of a maximum of five pixels, with a minimum 2-D correlation coefficient of 0.5. Sessions were aligned to session A(n), the last session in environment A. Calcium imaging (CaImg) enabled the longitudinal monitoring of the same hippocampal cells over multiple sessions in both environments. For criterion and testing sessions, we observed an average of 459.85±265.31 cells per session per animal, with no significant difference between the number of cells recorded in environment A and environment B (two-tailed t-test(24)= -0.56, p>0.05) On average, 132±95 cells were present in both the last criterion session in A and the first testing session in B. This was not a significantly different numbers of cells that were present, on average, in both the semi-final session in A, session A(n-1), and the final session in A, session A(n) (155±115 cells). Place cell identification and computing spatial mutual information Place cells were identified using mutual information computed when the animals were running at speeds greater than or equal to 4cm/s. MI was computed for all cells; there was no calcium event rate criterion for included cells. To be considered significant, the computed mutual information (MI) must be greater than 95% of MI scores computed 500 times from shuffled positions 12 . To compute the MI for each cell, the training environments were divided into 2.5cm x 2.5cm bins. The calcium event rate of each cell and the occupancy of the animal were found for each bin. Rate and occupancy were smoothed with a Gaussian kernel with filter width of 3cm and Sigma of 0.5cm. Mutual information was computed during periods of movement as follows 2 , 12 , 13 : where: P s = calcium event probability in each bin P o = occupancy probability at each bin Mutual information using calcium traces was computed as above, except instead of P s being calcium event probability per bin, the value of P s was the average value of calcium trace in the bin. We computed MI using both calcium events and calcium trace data. There was no significant difference between the number of place cells detected using calcium event data and calcium trace data, (paired t-test t(24)=1.01, p>0.05). Note that all place cell and place field measurements are presented with conditioning periods included, as the animal was frequently moving during conditioning periods. We also computed results while excluding conditioning periods and found no significant differences. Computing CSUS mutual information The computation of CSUS mutual information was very similarly to that for spatial mutual information. A 1.3 second period beginning at the start of the CS tone was either divided into 2 bins (CSUS-MI2) or 5 bins (CSUS-MI5) ( Fig. 3c - 3d ). Mutual information was then computed using the following: Where: P s = calcium event probability in each CSUS bin P o = probability of individual CSUS occuring out of all CSUS bins Mutual information using calcium traces was computed as above, except that P s did not represent the calcium event probability per bin, but the average value of calcium trace within the bin. Remapping quantification The place cell center was defined as the occupancy-normalized location with maximum number of calcium events while the animal was moving at 5cm/s or faster. Position was binned into 2.5cm square bins. The place cell centers at environments A and B, as well as at environment A across days and at environment B across days, were used to align each environment across days, as well as to align environment A to environment B. Population vector correlation was calculated between two environments using calcium event data. Neurons present in both datasets (such as sessions A(n) and A(n-1), or A(n) and B(1)) were identified and their calcium event times were converted to rates using 0.75 second binning. These firing rates were then normalized using z-score normalization across each neuron’s activity across time. The mean calcium event rate for each neuron in each environment was then computed. The correlation among population vectors of firing rates in the two environments was cpmputed: Where: 𝑟 { i,A } and 𝑟 { i,B } = firing rates of neuron i in environments A and B = mean firing rates across neurons in environments A and B The factors in the denominator correspond to the standard deviation of the components of each population vector relative to their mean, computed in each environment. Use of CEBRA versus alternative methods We explored multiple different methods before settling on the use of CEBRA for this study. A short summary of each tested method can be found below: Principal Component Analysis (PCA) 14 : Principal component analysis (PCA) is a statistical method used to reduce the dimensionality of data while retaining as much variability as possible. This linear technique identifies the axes (principal components) in the dataset that maximize variance. The first principal component explains the most variance, the second explains the second most, and so on. Principal components are combinations of original features and may not always have clear or intuitive meanings. In agreement with previous hippocampal data 15 , PCA required upwards of 15-25 components to capture 95% of the variance of the data. In addition, across and within all sessions and representations (spatial and task representations), the manifolds spanned by the largest PCs remained highly similar, with small principal angles in pairwise comparisons. This similarity in the orientation of the leading subspaces suggested that PCA did not distinguish between spatial or behavioral components of the task ( Figure S5 ). Independent Component Analysis ( ICA ) 16 : Independent Component Analysis (ICA) is a computational technique used to separate a multivariate signal into additive, independent components. ICA operates under the assumption that observed data are linear mixtures of underlying, independent sources. It aims to find a linear transformation that maximizes the statistical independence of the estimated components. We found that ICA embeddings were unstable throughout the length of the recordings, and also did not clearly map onto behavioral states ( Figure S6 ). Isomap 17 : Isomap is a manifold learning technique that seeks to capture the intrinsic geometric structure of data. Isomap is useful when linear methods like PCA cannot capture the intrinsic structure of the data, as it preserves the geodesic (curved) distances in the reduced dimensionality space. Unlike linear methods such as PCA, Isomap can capture nonlinear relationships in the data. Interestingly, using Isomap, only about 5 neural modes were required to achieve a residual variance of 5-10%. However, the embedding shape did not relate to any discernable property of neural data or behavior ( Figure S7 ). Dimensionality reduction was achieved, but the resulting representations were not interpretable ( Figure S7 ). MIND 15 , 18 : MIND is a decoding method designed for integrating multiple data modalities to predict various features, particularly sensory and motor functions. MIND uses recurrent neural networks whose hidden variables provide a memory mechanism for remembering previous inputs; this approach is particularly apt for the analysis of time series data such as neural recordings. While MIND was very robust at distinguishing the different environments, it was not equipped to handle relatively short signals separated in time, such as the conditioning trials separated by intertrial intervals. Our analyses using MIND resulted in poor and unstable embeddings that could not be analyzed ( Figure S8 ). CEBRA 19 was chosen for this project for its ability to capture nonlinear relationships in the data and to create stable embeddings over short and long time periods. Additionally, spatial separations of components were well isolated and correlated well with observed behaviors. Use of CEBRA for position decoding Optimal parameters for decoding the position of each animal from neural activity were determined using an extensive grid search across learning rate, temperature, and number of iterations ( Figure S9 ). Models created to compare different sessions, such as a model trained on data from session A(n) used to decode session B(1), were only trained on cells that occurred in both sessions. Models were trained on spike traces of these cells, labeled with the animal’s (X,Y) position. In all cases, 75% of data was used to train the model while 25% of data was held out for verification. All models were run 500 times. Optimal embeddings were determined based on the minimum median absolute error between the predicted and true positions. The optimal parameters for each rat are as follows: View this table: View inline View popup Download powerpoint The number of output dimensions was chosen based on the fewest number of dimensions under which all 5 models consistently outperformed shuffled data for both position and conditioning decoding ( Figure S10 - 12 ). Use of CEBRA for conditioning decoding As in position decoding, the optimal parameters for decoding conditioning were determined for each animal using an extensive grid search across learning rate, temperature, and number of iterations ( Figure S11 ). Models created to compare different sessions of neural activity, such as a model trained on data from session A(n) used to decode session B(1), were only trained on cells that occurred in both sessions. Models were trained on spike traces of these cells, with labels corresponding to the CSUS bin during which the signal occurred (either one out of 2 bins or out of 5 bins, see Figures 3c-d ). In all cases, 75% of data was used to train the model while 25% of data was held out for verification. All models were run 500 times. Optimal embeddings were determined based on the percent of correctly binned time points. The optimal parameters for each rat are as follows: View this table: View inline View popup The number of output dimension was chosen based on the fewest number of dimensions under which all 5 models consistently outperformed shuffled data for both position and conditioning decoding ( Figure S10 - 12 ). The parameters listed above were used for decoding into 2 or 5 bins, including the use of 3 output dimensions (# of latents). The accuracy of results was computed from the entries in the confusion matrix: Precision was calculated for each class 𝑖, 1 ≤ 𝑖 ≤ 𝑛, where 𝑛 is the number of classes: where: 𝑇𝑃 = 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 𝐹𝑃 = 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 The global precision is given by the average: Recall, also known as sensitivity, was calculated for each class 𝑖, 1 ≤ 𝑖 ≤ 𝑛, where 𝑛 is the number of classes: where: 𝑇𝑃 = 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 𝐹𝑁 = 𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠 The global recall is given by the average: The F1 score was calculated for each class 𝑖, 1 ≤ 𝑖 ≤ 𝑛, where 𝑛 is the number of classes: The global F1 score is given by the average: The area under the receiver operating characteristic (ROC) curve was calculated for each class 𝑖, 1 ≤ 𝑖 ≤ 𝑛, where 𝑛 is the number of classes: The global value is given by the average: where: 𝑦 true-bin = 𝑏𝑖𝑛𝑎𝑟𝑖𝑧𝑒𝑑 𝑡𝑟𝑢𝑒 𝑙𝑎𝑏𝑒𝑙𝑠 𝑦 pred-prob = 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑐𝑙𝑎𝑠𝑠 Model consistency Model consistency was computed using a built-in CEBRA function which relies on the function ‘sklearn.metrics.consistency_score’ 20 . The function compares the embeddings from different models by calculating pairwise consistency scores. This comparison involves measuring the similarity of the embeddings using statistical metrics; i.e. this metric calculates how similar a model’s labels are for similar instances in the data set. To determine consistency between environments and across animals, the data was fit to each model 20 times. The model with the lowest loss was selected and compared to other models with the lowest loss. Models were created using each individual animal’s optimal parameters (see above). Author contributions Investigation, H.S.W.; Formal Analysis, H.S.W.; Writing –Original Draft, H.S.W.; Writing – Review & Editing, H.S.W., S.A.S., J.F.D.; Funding: H.S.W, J.F.D., Supervision, S.A.S., J.F.D. Declaration of interests The authors declare no competing interests. Additional Information Supplementary Information is available for this paper. Correspondence and requests for materials should be addressed to Hannah S Wirtshafter, hsw{at}northwestern.edu . Reprints and permissions information is available at https://www.nature.com/reprints . Supplementary figures Download figure Open in new tab Figure S1. Photos of testing chambers. Photos of testing chambers. Top: Environment A, an unscented rectangular enclosure with wire floor and walls, and white lighting. Bottom: Environment B, a scented ovular enclosure with white solid floor and walls, and red lighting. Both environments were located at the same spot in the room relative to external cues. The door to the chamber was closed during testing, to accentuate the distinction between the white and red lighting. Download figure Open in new tab Figure S2. Learning curves for the five rats. Substantial variability in the number of sessions required to learn the task; average number of sessions was 20 ± 4.2 (including the criterion sessions). The fastest learners (2 rats) reached criterion after 14 sessions, while the slowest rat required 24 sessions to reach criterion. Download figure Open in new tab Figure S3. Place cells remap based on comparison to shuffled data. Distribution of the median distance between place field centers after shuffling center locations 500 times. The actual median value for session A(n) to A(n-1) was smaller than all shuffled medians (p = 0, dashed blue line), while the median for session A(n) to B(1) was greater than or equal to 56% of shuffled medians (p = 0.56, dashed yellow line). The brown shaded area represents the overlap between shuffled distributions. Download figure Open in new tab Figure S4. A model trained in environment A can decode positions within environment A but not in environment B. a. A model trained on calcium trace and position data from session A(n), using cells present in both A(n) and A(n-1), predicted positions in environment A(n-1) with significantly greater accuracy than that of a model trained on shuffled position data. The model was run 500 times, and the accuracy of predictions was assessed. The model trained on actual data significantly outperformed the shuffled model across rats (double-sided t-tests: Rat 1: t(998) = -34.3, p = 5.0 × 10 -171 ; Rat 2: t(998) = -72.5, p = 0; Rat 3: t(998) = -1.96, p = 0.05; Rat 4: t(998) = -53.7, p = 2.6 × 10 -299 ; Rat 5: t(998) = -20.0, p = 4.1 × 10 -75 ). Error bars represent standard error. A single asterisk (*) indicates p ≤ 0.05, and three asterisks (***) indicate p < 10⁻⁷⁴. b. (Top row) Visualization of model performance for decoding animal position in environment A(n). Left: The trained model demonstrated on the training data from session A(n). Middle: The same model applied to held-out trace data (25%) from session A(n). Right: The model applied to predict animal position in session A(n-1). (Bottom row) The same models trained on shuffled position data. Shown here is the model for Rat 4, where the model trained on real data significantly outperformed model trained on shuffled data for decoding position in session A(n-1) (500 simulations, t(998) = -53.7, p = 2.6 × 10 -299 ). For visualization purposes, distance from the corner of the environment is plotted using normalized values in arbitrary units [a.u.]. c. A model trained on data from session A(n), using cells present in both A(n) and B(1), was applied to environment B(1). The model’s predictions were significantly less accurate than those of a model trained on shuffled position data (double-sided t-tests: Rat 1: t(998) = 84.1, p = 0; Rat 2: t(998) = 18.8, p = 7.4 × 10 -68 ; Rat 3: t(998) = 74.7, p = 0; Rat 4: t(998) = 13.1, p = 2.0 × 10 -36 ; Rat 5: t(998) = 154.4, p = 0). Error bars represent standard error. Three asterisks (***) indicate p < 10 -35 . d. (Top row) Model trained on position and calcium trace data from session A(n), using cells present in both A(n) and B(1). Left: The model demonstrated on the training data from session A(n). Middle: The same model applied to held-out trace data (25%) from session A(n). Right: The model applied to predict the animal position in session B(1). (Bottom row) The same models trained on shuffled position data. Shown here is the model for Rat 4, where the shuffled model performed significantly better than the model trained on session A(n) for decoding position in session B(1) (500 simulations, t(998) = 13.1, p = 2.0 × 10 -36 ). For visualization purposes, distance from the corner of the environment is plotted using normalized values in arbitrary units [a.u.]. Download figure Open in new tab Figure S5. PCA computations for session A(n) and session B(1). Only cells present in both sessions were used. Principal component analysis (PCA) revealed that approximately 15-25 principal components (PCs) are needed to account for 95% of the variance in the data. When using the complete cell population (not shown), more than 25 PCs are required to achieve the same variance. Across and within all sessions and representations (spatial and task), the principal angles between manifolds remain highly similar. Download figure Open in new tab Figure S6. ICA computations across different segments of a session. Top: Independent component analysis (ICA) was for data over an entire session, using three independent components (ICs). Blue dots represent non-trial times, while red dots represent trial times. Middle: ICA computed over the last two-thirds of the same session shows variability in ICs across segments within a session. Bottom: ICA computed over the second half of the session shows additional variability in the results of the analysis depending on how the session is divided. These results indicate that independent components are highly variable over the course of one session and are sensitive to how the session is partitioned. Download figure Open in new tab Figure S7. Isomap computations for session A(n) and session B(1). Isomap computations suggest that approximately five neural modes are sufficient to achieve a residual variance of 5-10%. However, the shape of the Isomap embedding does not correlate with any discernable properties of neural activity or behavior, suggesting limited interpretability of the embedding structure. Download figure Open in new tab Figure S8. MIND outputs for sessions A(n), B(1), and concatenated sessions. Top row: MIND embeddings during movement, excluding trial periods, with color bars representing frames. The temporal structure of the data is well captured, with clear separation between A(n) and B(1). Bottom row: MIND embeddings during conditioning periods are highly unstable. Small changes in parameters result in substantial shifts in the embedding structure, transitioning from a linear structure (left) to an undefined, unstable cloud (middle and right). Download figure Open in new tab Figure S9. Grid search over decoding parameters for position. A grid search was performed over three parameters: minimum temperature, learning rate, and number of iterations, for decoding position. Models were trained using cells from session A(n) that also appeared in session A(n-1). The figure shows decoding accuracy for session A(n-1) using the models trained on data from session A(n). Yellow areas indicate higher decoding accuracy. Download figure Open in new tab Figure S10. Position decoding error as a function of the number of latents (Rat 5). This figure shows the decoding error for position as the number of latents increases. Left panels: model trained on data from A(n), tested on held out data from A(n). Upper left panel: As the number of latents increases, the model’s ability to decode a different session within the same environment decreases. This effect is not consistent across all rats; see lower left panel. Right panels: model trained on data from A(n), tested on A(n-1) (top) or B(1) (bottom). Performance is particularly bad when the model is tested on a different environment (lower right panel). Download figure Open in new tab Figure S11. CSUS2 decoding accuracy with increasing number of latents (Rat 3). Percent of incorrect decoding for the CSUS2 model as the number of latents increases. Left panels: model trained on data from A(n), tested on held out data from A(n). As the number of latents increases, the model’s ability to decode a different session within the same environment remains stable or slightly decreases. Right panels: model trained on data from A(n), tested on A(n-1) (top) or B(1) (bottom). Performance remains stable for a different session in the same environment (upper right panel) but deteriorates with increasing number of latents when the model is tested in a different environment (lower right panel). Each model was run 100 times. Download figure Open in new tab Figure S12. CSUS5 decoding accuracy with increasing number of latents (Rat 5). Same as Figure S11 , but for CSUS5; the conditioning period has been divided into five time bins instead of two. The percent of incorrect decoding is shown as the number of latents increases. Left panels: model trained on data from A(n), tested on held out data from A(n). As the number of latents increases, the model’s ability to decode a different session within the same environment remains stable or slightly increases. Right panels: model trained on data from A(n), tested on A(n-1) (top) or B(1) (bottom). Performance remains stable for a different session in the same environment (upper right panel) but deteriorates with increasing number of latents when the model is tested in a different environment (lower right panel). Each model was run 100 times. Download figure Open in new tab Figure S13. Grid search over decoding parameters for conditioning. A grid search was performed over three parameters: minimum temperature, learning rate, and number of iterations for conditioning decoding. Models were trained using cells from session A(n) that also appeared in session B(1). The figure shows decoding accuracy for CSUS2 session B(1) using the models trained on data from session A(n). Yellow areas indicate higher decoding accuracy. For Rats 1 and 4, the ‘Euclidean’ distance with ‘constant’ temperature mode was used. For Rats 2 and 5, ‘cosine’ distance with ‘constant’ temperature mode was used. For Rat 3, ‘cosine’ distance with ‘auto’ temperature mode was used. Acknowledgements This work was supported by an NIA T32 (T32-AG020506/AG/NIA), an NIA R37 (R37-AG008796/AG/NIA), an NINDS R01 (R01 NS113804/NS/NINDS), and a K99 award (K99 MH135062). This research was supported in part through the computational resources and staff assistance provided by the Quest high performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. We would like to thank all members of the Disterhoft lab, especially Mackenzie Kneisly. We would like to thank the following individuals for their assistance with CEBRA: Mackenzie Mathis and Steffen Schneider. And additional thank you to David Wirtshafter for his feedback. Funder Information Declared National Institute of Mental Health , K99 MH135062 National Institute on Aging , R37 AG008796/AG/NIA National Institute of Neurological Disorders and Stroke , R01 NS113804/NS/NINDS Footnotes ↵ * Lead contact: hsw{at}northwestern.edu (HSW) Citation additions for related work and proper upload of supp material Citations ↵ Bouton , M. E . A learning theory perspective on lapse, relapse, and the maintenance of behavior change . Health psychology 19 , 57 ( 2000 ). Herszage , J. & Censor , N . Modulation of learning and memory: a shared framework for interference and generalization . Neuroscience 392 , 270 – 280 ( 2018 ). OpenUrl CrossRef PubMed Linda , Q. Y. , Wilson , R. C. & Nassar , M. R . Adaptive learning is structure learning in time . Neuroscience & Biobehavioral Reviews 128 , 270 – 281 ( 2021 ). OpenUrl PubMed ↵ Censor , N . Generalization of perceptual and motor learning: a causal link with memory encoding and consolidation? Neuroscience 250 , 201 – 207 ( 2013 ). OpenUrl CrossRef PubMed ↵ Brown , S. & Bebko , J . Generalization, overselectivity, and discrimination in the autism phenotype: A review . Research in Autism Spectrum Disorders 6 , 733 – 740 ( 2012 ). OpenUrl ↵ Church , B. A. et al. Learning, plasticity, and atypical generalization in children with autism . Psychonomic Bulletin & Review 22 , 1342 – 1348 ( 2015 ). OpenUrl PubMed ↵ Weiler , J. A. , Bellebaum , C. , Brüne , M. , Juckel , G. & Daum , I . Impairment of probabilistic reward-based learning in schizophrenia . Neuropsychology 23 , 571 ( 2009 ). ↵ Shohamy , D. et al. Learning and generalization in schizophrenia: effects of disease and antipsychotic drug treatment . Biological psychiatry 67 , 926 – 932 ( 2010 ). OpenUrl CrossRef PubMed Web of Science ↵ Anastasides , N. et al. Increased generalization of learned associations is related to re-experiencing symptoms in veterans with symptoms of post-traumatic stress . Stress 18 , 484 – 489 ( 2015 ). OpenUrl CrossRef PubMed ↵ Lis , S. et al. Generalization of fear in post-traumatic stress disorder . Psychophysiology 57 , e13422 ( 2020 ). OpenUrl CrossRef PubMed ↵ Calandreau , L. , Desgranges , B. , Jaffard , R. & Desmedt , A . Switching from contextual to tone fear conditioning and vice versa: the key role of the glutamatergic hippocampal-lateral septal neurotransmission . Learn Mem 17 , 440 – 443 ( 2010 ). doi: 10.1101/lm.1859810 OpenUrl Abstract / FREE Full Text Desmedt , A. , Garcia , R. & Jaffard , R . Vasopressin in the lateral septum promotes elemental conditioning to the detriment of contextual fear conditioning in mice . Eur J Neurosci 11 , 3913 – 3921 ( 1999 ). OpenUrl CrossRef PubMed Web of Science Vouimba , R. M. , Garcia , R. & Jaffard , R . Opposite effects of lateral septal LTP and lateral septal lesions on contextual fear conditioning in mice . Behav Neurosci 112 , 875 – 884 ( 1998 ). OpenUrl CrossRef PubMed Anagnostaras , S. G. , Gale , G. D. & Fanselow , M. S . Hippocampus and contextual fear conditioning: recent controversies and advances . Hippocampus 11 , 8 – 17 ( 2001 ). doi: 10.1002/1098-1063(2001)11:13.0.CO;2-7 OpenUrl CrossRef PubMed Web of Science Honey , R. C. & Good , M . Selective hippocampal lesions abolish the contextual specificity of latent inhibition and conditioning . Behav Neurosci 107 , 23 – 33 ( 1993 ). doi: 10.1037//0735-7044.107.1.23 OpenUrl CrossRef PubMed Web of Science Plitt , M. H. & Giocomo , L. M . Experience-dependent contextual codes in the hippocampus . Nat Neurosci 24 , 705 – 714 ( 2021 ). doi: 10.1038/s41593-021-00816-6 OpenUrl CrossRef PubMed ↵ Devito , L. M. , Kanter , B. R. & Eichenbaum , H . The hippocampus contributes to memory expression during transitive inference in mice . Hippocampus 20 , 208 – 217 ( 2010 ). doi: 10.1002/hipo.20610 OpenUrl CrossRef PubMed Web of Science ↵ Schimanski , L. A. , Lipa , P. & Barnes , C. A . Tracking the course of hippocampal representations during learning: when is the map required? J Neurosci 33 , 3094 – 3106 ( 2013 ). doi: 10.1523/JNEUROSCI.1348-12.2013 OpenUrl Abstract / FREE Full Text Gauthier , J. L. & Tank , D. W . A Dedicated Population for Reward Coding in the Hippocampus . Neuron 99 , 179 – 193 e177 ( 2018 ). doi: 10.1016/j.neuron.2018.06.008 OpenUrl CrossRef PubMed Mamad , O. et al. Place field assembly distribution encodes preferred locations . PLoS Biol 15 , e2002365 ( 2017 ). doi: 10.1371/journal.pbio.2002365 OpenUrl CrossRef PubMed Hok , V. et al. Goal-related activity in hippocampal place cells . J Neurosci 27 , 472 – 482 ( 2007 ). 27/3/472 [pii] doi: 10.1523/JNEUROSCI.2864-06.2007 OpenUrl Abstract / FREE Full Text ↵ Knierim , J. J . Dynamic interactions between local surface cues, distal landmarks, and intrinsic circuitry in hippocampal place cells . J Neurosci 22 , 6254 – 6264 ( 2002 ). 20026608 OpenUrl Abstract / FREE Full Text ↵ Allen , K. , Rawlins , J. N. , Bannerman , D. M. & Csicsvari , J . Hippocampal place cells can encode multiple trial-dependent features through rate remapping . J Neurosci 32 , 14752 – 14766 ( 2012 ). doi: 10.1523/JNEUROSCI.6175-11.2012 OpenUrl Abstract / FREE Full Text ↵ Jeffery , K. J. , Gilbert , A. , Burton , S. & Strudwick , A . Preserved performance in a hippocampal-dependent spatial task despite complete place cell remapping . Hippocampus 13 , 175 – 189 ( 2003 ). doi: 10.1002/hipo.10047 OpenUrl CrossRef PubMed Web of Science ↵ Colgin , L. L. , Moser , E.I. , May-Britt Moser . Understanding memory through hippocampal remapping . Cell ( 2008 ). ↵ Christian , K. M. & Thompson , R. F . Neural substrates of eyeblink conditioning: acquisition and retention . Learn Mem 10 , 427 – 455 ( 2003 ). doi: 10.1101/lm.59603 OpenUrl Abstract / FREE Full Text McEchron , M. D. , Tseng , W. & Disterhoft , J. F . Single neurons in CA1 hippocampus encode trace interval duration during trace heart rate (fear) conditioning in rabbit . J Neurosci 23 , 1535 – 1547 ( 2003 ). OpenUrl Abstract / FREE Full Text Wirtshafter , H. S. & Wilson , M. A . Locomotor and Hippocampal Processing Converge in the Lateral Septum . Current biology : CB 29 , 3177 – 3192 ( 2019 ). doi: 10.1016/j.cub.2019.07.089 OpenUrl CrossRef PubMed Berger , T. W. , Rinaldi , P. C. , Weisz , D. J. & Thompson , R. F . Single-unit analysis of different hippocampal cell types during classical conditioning of rabbit nictitating membrane response . J Neurophysiol 50 , 1197 – 1219 ( 1983 ). doi: 10.1152/jn.1983.50.5.1197 OpenUrl CrossRef PubMed Web of Science Wirtshafter , H. S. & Wilson , M. A . Differences in reward biased spatial representations in the lateral septum and hippocampus . eLife 9 ( 2020 ). doi: 10.7554/eLife.55252 OpenUrl CrossRef ↵ Wirtshafter , H. S. , Mehta , M. R. , Solla , S. A. & Disterhoft , J. F . Hippocampal trace coding dominates and disrupts place coding . bioRxiv , 2026.2002.2017. 706430 ( 2026 ). doi: 10.64898/2026.02.17.706430 OpenUrl Abstract / FREE Full Text ↵ Aharoni , D. & Hoogland , T. M . Circuit Investigations With Open-Source Miniaturized Microscopes: Past, Present and Future . Frontiers in cellular neuroscience 13 , 141 ( 2019 ). doi: 10.3389/fncel.2019.00141 OpenUrl CrossRef PubMed Aharoni , D. , Khakh , B. S. , Silva , A. J. & Golshani , P . All the light that we can see: a new era in miniaturized microscopy . Nat Methods 16 , 11 – 13 ( 2019 ). doi: 10.1038/s41592-018-0266-x OpenUrl CrossRef PubMed Silva , A. J . Miniaturized two-photon microscope: seeing clearer and deeper into the brain . Light: Science & Applications 6 , e17104 – e17104 ( 2017 ). OpenUrl Wirtshafter , H. S. & Disterhoft , J. F . In Vivo Multi-Day Calcium Imaging of CA1 Hippocampus in Freely Moving Rats Reveals a High Preponderance of Place Cells with Consistent Place Fields . J Neurosci ( 2022 ). doi: 10.1523/JNEUROSCI.1750-21.2022 OpenUrl Abstract / FREE Full Text Cunningham , J. P. & Yu , B . Dimensionality reduction for large-scale neural recordings . Nature neuroscience 17 , 1500 – 1509 ( 2014 ). OpenUrl CrossRef PubMed ↵ Bassett , D. S. & Sporns , O. Network neuroscience . Nature neuroscience 20 , 353 – 364 ( 2017 ). OpenUrl CrossRef PubMed ↵ Fenton , A. A . Remapping revisited: how the hippocampus represents different spaces . Nat Rev Neurosci 25 , 428 – 448 ( 2024 ). doi: 10.1038/s41583-024-00817-x OpenUrl CrossRef PubMed ↵ Sanders , H. , Wilson , M. A. & Gershman , S. J . Hippocampal remapping as hidden state inference . eLife 9 ( 2020 ). doi: 10.7554/eLife.51140 OpenUrl CrossRef ↵ Bouton , M. E. , Nelson , J. B. & Rosas , J. M . Stimulus generalization, context change, and forgetting . Psychological bulletin 125 , 171 – 186 ( 1999 ). doi: 10.1037/0033-2909.125.2.171 OpenUrl CrossRef PubMed Web of Science ↵ Lovibond , P. F. , Preston , G. & Mackintosh , N . Context specificity of conditioning, extinction, and latent inhibition . Journal of Experimental Psychology: Animal Behavior Processes 10 , 360 ( 1984 ). ↵ Moyer , J. R. , Jr. , Deyo , R. A. & Disterhoft , J. F . Hippocampectomy disrupts trace eye-blink conditioning in rabbits . Behav Neurosci 104 , 243 – 252 ( 1990 ). OpenUrl CrossRef PubMed Web of Science McEchron , M. D. & Disterhoft , J. F . Hippocampal encoding of non-spatial trace conditioning . Hippocampus 9 , 385 – 396 ( 1999 ). doi: 10.1002/(SICI)1098-1063(1999)9:43.0.CO;2-K OpenUrl CrossRef PubMed Web of Science Woodruff-Pak , D. S. & Disterhoft , J. F . Where is the trace in trace conditioning? Trends Neurosci 31 , 105 – 112 ( 2008 ). doi: 10.1016/j.tins.2007.11.006 OpenUrl CrossRef PubMed Web of Science ↵ Weiss , C. & Disterhoft , J. F . The impact of hippocampal lesions on trace-eyeblink conditioning and forebrain-cerebellar interactions . Behav Neurosci 129 , 512 – 522 ( 2015 ). doi: 10.1037/bne0000061 OpenUrl CrossRef PubMed ↵ Whittington , J. C. R. et al. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation . Cell 183 , 1249 – 1263 e1223 ( 2020 ). doi: 10.1016/j.cell.2020.10.024 OpenUrl CrossRef PubMed ↵ Wirtshafter , H. S. & Wilson , M. A . Artificial intelligence insights into hippocampal processing . Front Comput Neurosci 16 , 1044659 ( 2022 ). doi: 10.3389/fncom.2022.1044659 OpenUrl CrossRef PubMed ↵ Lehnert , L. , Littman , M. L. & Frank , M. J . Reward-predictive representations generalize across tasks in reinforcement learning . PLoS Comput Biol 16 , e1008317 ( 2020 ). doi: 10.1371/journal.pcbi.1008317 OpenUrl CrossRef PubMed ↵ Collins , A. G. E. & Frank , M. J . Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning . Cognition 152 , 160 – 169 ( 2016 ). OpenUrl CrossRef PubMed ↵ Stachenfeld , K. L. , Botvinick , M. M. & Gershman , S. J . The hippocampus as a predictive map . Nat Neurosci 20 , 1643 – 1653 ( 2017 ). doi: 10.1038/nn.4650 OpenUrl CrossRef PubMed Eichenbaum , H . Hippocampus: cognitive processes and neural representations that underlie declarative memory . Neuron 44 , 109 – 120 ( 2004 ). OpenUrl CrossRef PubMed Web of Science Wills , T. J. , Lever , C. , Cacucci , F. , Burgess , N. & O’Keefe , J . Attractor dynamics in the hippocampal representation of the local environment . Science 308 , 873 – 876 ( 2005 ). doi: 10.1126/science.1108905 OpenUrl Abstract / FREE Full Text Colgin , L. L. et al. Attractor-map versus autoassociation based attractor dynamics in the hippocampal network . J Neurophysiol 104 , 35 – 50 ( 2010 ). doi: 10.1152/jn.00202.2010 OpenUrl CrossRef PubMed Web of Science ↵ Smith , D. M. & Mizumori , S. J . Learning-related development of context-specific neuronal responses to places and events: the hippocampal role in context processing . J Neurosci 26 , 3154 – 3163 ( 2006 ). doi: 10.1523/JNEUROSCI.3234-05.2006 OpenUrl Abstract / FREE Full Text ↵ Colgin , L. L. et al. Attractor-map versus autoassociation based attractor dynamics in the hippocampal network . Journal of neurophysiology 104 , 35 – 50 ( 2010 ). OpenUrl CrossRef PubMed Web of Science ↵ Lee , I. , Yoganarasimha , D. , Rao , G. & Knierim , J. J . Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3 . Nature 430 , 456 – 459 ( 2004 ). OpenUrl CrossRef PubMed Web of Science ↵ Bahar , A. S. , Shirvalkar , P. R. & Shapiro , M. L . Memory-guided learning: CA1 and CA3 neuronal ensembles differentially encode the commonalities and differences between situations . J Neurosci 31 , 12270 – 12281 ( 2011 ). doi: 10.1523/JNEUROSCI.1671-11.2011 OpenUrl Abstract / FREE Full Text Markus , E. J. et al. Interactions between location and task affect the spatial and directional firing of hippocampal neurons . J Neurosci 15 , 7079 – 7094 ( 1995 ). OpenUrl Abstract / FREE Full Text Singer , A. C. , Karlsson , M. P. , Nathe , A. R. , Carr , M. F. & Frank , L. M . Experience-dependent development of coordinated hippocampal spatial activity representing the similarity of related locations . J Neurosci 30 , 11586 – 11604 ( 2010 ). doi: 10.1523/JNEUROSCI.0926-10.2010 OpenUrl Abstract / FREE Full Text ↵ McKenzie , S. et al. Hippocampal representation of related and opposing memories develop within distinct, hierarchically organized neural schemas . Neuron 83 , 202 – 215 ( 2014 ). doi: 10.1016/j.neuron.2014.05.019 OpenUrl CrossRef PubMed Web of Science ↵ Eichenbaum , H. , Dudchenko , P. , Wood , E. , Shapiro , M. & Tanila , H . The hippocampus, memory, and place cells: is it spatial memory or a memory space? Neuron 23 , 209 – 226 ( 1999 ). https://doi.org:S0896-6273(00)80773-4 [pii] OpenUrl CrossRef PubMed Web of Science O’Keefe , J. & Krupic , J . Do hippocampal pyramidal cells respond to nonspatial stimuli? Physiol Rev 101 , 1427 – 1456 ( 2021 ). doi: 10.1152/physrev.00014.2020 OpenUrl CrossRef PubMed ↵ Knierim , J. J . From the GPS to HM: Place cells, grid cells, and memory . Hippocampus 25 , 719 – 725 ( 2015 ). doi: 10.1002/hipo.22453 OpenUrl CrossRef PubMed ↵ Nieh , E. H. et al. Geometry of abstract learned knowledge in the hippocampus . Nature 595 , 80 – 84 ( 2021 ). OpenUrl CrossRef PubMed ↵ Johnston , W. J. , Fine , J. M. , Yoo , S. B. M. , Ebitz , R. B. & Hayden , B. Y . Semi-orthogonal subspaces for value mediate a binding and generalization trade-off . Nature Neuroscience , 1 – 13 ( 2024 ). Machens , C. K. , Romo , R. & Brody , C. D . Functional, but not anatomical, separation of “what” and “when” in prefrontal cortex . Journal of Neuroscience 30 , 350 – 360 ( 2010 ). OpenUrl Abstract / FREE Full Text ↵ Mante , V. , Sussillo , D. , Shenoy , K. V. & Newsome , W. T . Context-dependent computation by recurrent dynamics in prefrontal cortex . nature 503 , 78 – 84 ( 2013 ). OpenUrl CrossRef PubMed Web of Science ↵ Flesch , T. , Juechems , K. , Dumbalska , T. , Saxe , A. & Summerfield , C . Orthogonal representations for robust context-dependent task performance in brains and neural networks . Neuron 110 , 1258 – 1270 . e1211 ( 2022 ). OpenUrl CrossRef PubMed ↵ Poulter , S. , Hartley , T. & Lever , C . The neurobiology of mammalian navigation . Current Biology 28 , R1023 – R1042 ( 2018 ). OpenUrl CrossRef PubMed Tucker , M. A. , Ord , T. J. & Rogers , T. L . Evolutionary predictors of mammalian home range size: body mass, diet and the environment . Global Ecology and Biogeography 23 , 1105 – 1114 ( 2014 ). OpenUrl Schilder , B. M. , Petry , H. M. & Hof , P. R . Evolutionary shifts dramatically reorganized the human hippocampal complex . Journal of Comparative Neurology 528 , 3143 – 3170 ( 2020 ). OpenUrl CrossRef PubMed Sherry , D. F. , Jacobs , L. F. & Gaulin , S. J . Spatial memory and adaptive specialization of the hippocampus . Trends in neurosciences 15 , 298 – 303 ( 1992 ). OpenUrl CrossRef PubMed Web of Science ↵ Pravosudov , V. V. & Roth II , T. C . Cognitive ecology of food hoarding: the evolution of spatial memory and the hippocampus. Annual Review of Ecology , Evolution, and Systematics 44 , 173 – 193 ( 2013 ). OpenUrl CrossRef ↵ Safaie , M. et al. Preserved neural dynamics across animals performing similar behaviour . Nature 623 , 765 – 771 ( 2023 ). doi: 10.1038/s41586-023-06714-0 OpenUrl CrossRef Gallego , J. A. et al. Cortical population activity within a preserved neural manifold underlies multiple motor behaviors . Nature communications 9 , 1 – 13 ( 2018 ). OpenUrl PubMed Talpir , I. & Livneh , Y . Stereotyped goal-directed manifold dynamics in the insular cortex . Cell reports 43 ( 2024 ). Harris , K. D. & Shepherd , G. M . The neocortical circuit: themes and variations . Nature neuroscience 18 , 170 – 181 ( 2015 ). OpenUrl CrossRef PubMed Shmuelof , L. & Krakauer , J. W . Are we ready for a natural history of motor learning? Neuron 72 , 469 – 476 ( 2011 ). OpenUrl CrossRef PubMed Web of Science ↵ Manoli , D. S. , Meissner , G. W. & Baker , B. S . Blueprints for behavior: genetic specification of neural circuitry for innate behaviors . Trends in neurosciences 29 , 444 – 451 ( 2006 ). OpenUrl CrossRef PubMed Web of Science Tierney , A . Evolutionary implications of neural circuit structure and function . Behavioural processes 35 , 173 – 182 ( 1995 ). OpenUrl CrossRef PubMed Web of Science ↵ LeDoux , J. E . As soon as there was life, there was danger: the deep history of survival behaviours and the shallower history of consciousness . Philosophical Transactions of the Royal Society B 377 , 20210292 ( 2022 ). ↵ Martínez-García , F. & Lanuza , E . Evolution of vertebrate survival circuits . Current Opinion in Behavioral Sciences 24 , 113 – 123 ( 2018 ). OpenUrl ↵ Kogan , E. , Lu , J. & Zuo , Y . Cortical circuit dynamics underlying motor skill learning: from rodents to humans . Frontiers in Molecular Neuroscience 16 , 1292685 ( 2023 ). ↵ LeDoux , J. & Daw , N. D . Surviving threats: neural circuit and computational implications of a new taxonomy of defensive behaviour . Nature Reviews Neuroscience 19 , 269 – 282 ( 2018 ). OpenUrl CrossRef PubMed ↵ Buzsáki , G. & Moser , E. I . Memory, navigation and theta rhythm in the hippocampal-entorhinal system . Nature neuroscience 16 , 130 – 138 ( 2013 ). OpenUrl CrossRef PubMed Sweis , B. M. , Mau , W. , Rabinowitz , S. & Cai , D. J . Dynamic and heterogeneous neural ensembles contribute to a memory engram . Curr Opin Neurobiol 67 , 199 – 206 ( 2021 ). doi: 10.1016/j.conb.2020.11.017 OpenUrl CrossRef PubMed Davidson , T. J. , Kloosterman , F. & Wilson , M. A . Hippocampal replay of extended experience . Neuron 63 , 497 – 507 ( 2009 ). doi: 10.1016/j.neuron.2009.07.027 S0896-6273(09)00582-0 [pii] OpenUrl CrossRef PubMed Web of Science ↵ Rubin , R. D. , Watson , P. D. , Duff , M. C. & Cohen , N. J . The role of the hippocampus in flexible cognition and social behavior . Frontiers in human neuroscience 8 , 742 ( 2014 ). Methods References ↵ Wirtshafter , H. S. & Disterhoft , J. F . Place cells are nonrandomly clustered by field location in CA1 hippocampus . Hippocampus 33 , 65 – 84 ( 2023 ). doi: 10.1002/hipo.23489 OpenUrl CrossRef PubMed ↵ Wirtshafter , H. S. & Disterhoft , J. F . In Vivo Multi-Day Calcium Imaging of CA1 Hippocampus in Freely Moving Rats Reveals a High Preponderance of Place Cells with Consistent Place Fields . J Neurosci ( 2022 ). doi: 10.1523/JNEUROSCI.1750-21.2022 OpenUrl Abstract / FREE Full Text ↵ Weiss , C. & Thompson , R. F . The effects of age on eyeblink conditioning in the freely moving Fischer-344 rat . Neurobiol Aging 12 , 249 – 254 ( 1991 ). doi: 10.1016/0197-4580(91)90105-s OpenUrl CrossRef PubMed Web of Science ↵ Skelton , R. W . Bilateral cerebellar lesions disrupt conditioned eyelid responses in unrestrained rats . Behav Neurosci 102 , 586 – 590 ( 1988 ). doi: 10.1037//0735-7044.102.4.586 OpenUrl CrossRef PubMed Web of Science ↵ Aharoni , D. , Khakh , B. S. , Silva , A. J. & Golshani , P . All the light that we can see: a new era in miniaturized microscopy . Nat Methods 16 , 11 – 13 ( 2019 ). doi: 10.1038/s41592-018-0266-x OpenUrl CrossRef PubMed ↵ Silva , A. J . Miniaturized two-photon microscope: seeing clearer and deeper into the brain . Light: Science & Applications 6 , e17104 – e17104 ( 2017 ). OpenUrl ↵ Mathis , A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning . Nature neuroscience 21 , 1281 – 1289 ( 2018 ). OpenUrl CrossRef PubMed ↵ Corder , G. et al. An amygdalar neural ensemble that encodes the unpleasantness of pain . Science 363 , 276 – 281 ( 2019 ). doi: 10.1126/science.aap8586 OpenUrl Abstract / FREE Full Text ↵ Thevenaz , P. , Ruttimann , U. E. & Unser , M . A pyramid approach to subpixel registration based on intensity . IEEE Trans Image Process 7 , 27 – 41 ( 1998 ). doi: 10.1109/83.650848 OpenUrl CrossRef PubMed Web of Science ↵ Zhou , P. et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data . eLife 7 ( 2018 ). doi: 10.7554/eLife.28728 OpenUrl CrossRef PubMed ↵ CIAtah: a software package for analyzing one- and two-photon calcium imaging datasets . v. v1.0.0 (Zenodo , 2018 ). ↵ Kinsky , N. R. , Sullivan , D. W. , Mau , W. , Hasselmo , M. E. & Eichenbaum , H. B . Hippocampal Place Fields Maintain a Coherent and Flexible Map across Long Timescales . Current biology : CB 28 , 3578 – 3588 e3576 ( 2018 ). doi: 10.1016/j.cub.2018.09.037 OpenUrl CrossRef PubMed ↵ Olypher , A. V. , Lansky , P. , Muller , R. U. & Fenton , A. A . Quantifying location-specific information in the discharge of rat hippocampal place cells . J Neurosci Methods 127 , 123 – 135 ( 2003 ). doi: 10.1016/s0165-0270(03)00123-7 OpenUrl CrossRef PubMed Web of Science ↵ Pearson , K. LIII . On lines and planes of closest fit to systems of points in space. The London , Edinburgh, and Dublin philosophical magazine and journal of science 2 , 559 – 572 ( 1901 ). OpenUrl CrossRef ↵ Nieh , E. H. et al. Geometry of abstract learned knowledge in the hippocampus . Nature 595 , 80 – 84 ( 2021 ). OpenUrl CrossRef PubMed ↵ Ans , B. , Hérault , J. & Jutten , C . Architectures neuromimétiques adaptatives: Détection de primitives . Proceedings of Cognitiva 85 , 593 – 597 ( 1985 ). OpenUrl ↵ Tenenbaum , J. B. , Silva , V. d. & Langford , J. C. A global geometric framework for nonlinear dimensionality reduction . science 290 , 2319 – 2323 ( 2000 ). OpenUrl Abstract / FREE Full Text ↵ Low , R. J. , Lewallen , S. , Aronov , D. , Nevers , R. & Tank , D. W . Probing variability in a cognitive map using manifold inference from neural dynamics . BioRxiv , 418939 ( 2018 ). ↵ Schneider , S. , Lee , J. H. & Mathis , M. W . Learnable latent embeddings for joint behavioural and neural analysis . Nature 617 , 360 – 368 ( 2023 ). doi: 10.1038/s41586-023-06031-6 OpenUrl CrossRef PubMed ↵ Pedregosa , F. et al. Scikit-learn: Machine learning in Python . the Journal of machine Learning research 12 , 2825 – 2830 ( 2011 ). OpenUrl View the discussion thread. Back to top Previous Next Posted February 19, 2026. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Conserved hippocampal population geometry supports task generalization Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Conserved hippocampal population geometry supports task generalization Hannah S Wirtshafter , Sara A Solla , John F Disterhoft bioRxiv 2024.10.24.620127; doi: https://doi.org/10.1101/2024.10.24.620127 Share This Article: Copy Citation Tools Conserved hippocampal population geometry supports task generalization Hannah S Wirtshafter , Sara A Solla , John F Disterhoft bioRxiv 2024.10.24.620127; doi: https://doi.org/10.1101/2024.10.24.620127 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7649) Biochemistry (17738) Bioengineering (13925) Bioinformatics (42059) Biophysics (21496) Cancer Biology (18643) Cell Biology (25577) Clinical Trials (138) Developmental Biology (13406) Ecology (19946) Epidemiology (2067) Evolutionary Biology (24370) Genetics (15627) Genomics (22551) Immunology (17772) Microbiology (40497) Molecular Biology (17212) Neuroscience (88786) Paleontology (667) Pathology (2845) Pharmacology and Toxicology (4835) Physiology (7663) Plant Biology (15177) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9838) Zoology (2272)