HippocampusPub Date : 2026-04-13DOI: 10.1002/hipo.70093
Aditya Srinivasan, Aditya Behal, Kevin Guise, Matthew L. Shapiro
{"title":"Subsets of Single Neurons Predict Ensemble Activity and Memory Choices","authors":"Aditya Srinivasan, Aditya Behal, Kevin Guise, Matthew L. Shapiro","doi":"10.1002/hipo.70093","DOIUrl":"10.1002/hipo.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>Finding elements of a complex network which contribute most to the network's overall behavior is an open problem in various fields. This challenge is particularly difficult in neuroscience as it requires identifying which of a mammalian brain's many millions of neurons inform specific behavioral choices. Using methods inspired by compressed sensing, we identified subsets of CA1 neuronal ensembles recorded while only male rats performed spatial memory and cue-approach tasks in a plus maze. These subsets consisted of the units with firing rates which co-varied most closely with overall ensemble activity. Unit activity from these predictive subsets asymmetrically predicted the activity of other units in the ensemble. Excluding the predictive subset had no effect on ensemble decoding of the rat's current location but reduced decoding of past and future locations, suggesting that the predictive subset encodes nonlocal information. Predictive subsets likely represent a hierarchical and sparse coding scheme used by CA1, and further investigation of the properties of these sub-populations may lead to additional insights into the basic computational processes of the brain.</p>\u0000 </div>","PeriodicalId":13171,"journal":{"name":"Hippocampus","volume":"36 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147672805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HippocampusPub Date : 2026-04-11DOI: 10.1002/hipo.70089
Lior Baron, Kamran Diba, Asohan Amarasingham
{"title":"The Role of Plasticity in Replay: Stability Through Anti-Hebbian Rules","authors":"Lior Baron, Kamran Diba, Asohan Amarasingham","doi":"10.1002/hipo.70089","DOIUrl":"10.1002/hipo.70089","url":null,"abstract":"<p>Hippocampal replay is now considered to be a cornerstone of memory consolidation, yet the synaptic plasticity rules governing its dynamics remain elusive. Under the standard asymmetric Hebbian spike-timing dependent plasticity (STDP) model, the same spike patterns that promote activity propagation along one direction of sequential activation undermine propagation in the reverse direction, compromising “bidirectional” replay. On the other hand, symmetric potentiation rules, as recently proposed for region CA3, risk corrupting the memory trace by saturating synaptic weights. Using Ecker et al.'s recurrent network model of place cells that spontaneously generate replays during ripples, we systematically investigated how different STDP plasticity rules modulate offline replays. We developed a classification framework to study the mechanisms relating different STDP kernels to key replay characteristics, including directionality, speed, and stability. Our results confirmed that symmetric potentiation rules during offline states saturate synapses, inducing rigid attractors that corrupt the memory trace, and that an asymmetric Hebbian STDP kernel induces strong biases in the directionality of replay, leading to rapid replay acceleration and replay degradation. Notably, we found that an asymmetric anti-Hebbian STDP kernel preserves replay bi-directionality and stabilizes replay speed. We further identified the negative timing component of the STDP rule as the primary driver of replay speed: potentiation causes deceleration, while depression causes acceleration. These findings provide a mechanistic explanation for empirically observed replay deceleration and suggest a role for anti-Hebbian synaptic depression in stabilizing replay dynamics.</p>","PeriodicalId":13171,"journal":{"name":"Hippocampus","volume":"36 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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