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Topic: hippocampal place codes

ePoster
3 ePosters

In Neuroscience

ePoster · Neuroscience

Paradoxical effect of exercise on the long-term stability of hippocampal place code

Yoav Rechavi,Alon Rubin,Yaniv Ziv · COSYNE 2022

Thu, Mar 17, 2022

In addition to maintaining a healthy body, physical activity benefits the brain, improving memory and cognition. While some of the physiological effects of voluntary exercise on the brain are known (e.g., increased hippocampal neurogenesis), little is known about its effects on the neural code. Using longitudinal Ca2+ imaging in freely behaving mice and tracking the same neurons over weeks, we studied how voluntary exercise affects the quality and long-term stability of hippocampal place codes. In addition to increasing neurogenesis, we found that running accelerated the emergence of spatial c

ePoster · Neuroscience

Isolating the role of synaptic plasticity in hippocampal place codes

Mark Plitt,Konstantin Kaganovsky,Jun Ding,Thomas Sudhof,Lisa Giocomo · COSYNE 2022

Long-term potentiation (LTP) is thought to be a key plasticity mechanism underlying the formation and maintenance of hippocampal place codes. It is unclear, however, which properties of these neural codes are inherited from upstream processing and which are learned through plasticity. To disentangle these influences on place codes, we utilized a method for abolishing LTP specifically in hippocampal region CA1 of the adult mouse without affecting basal synaptic processing: conditional genetic deletion of a necessary component of the postsynaptic membrane fusion machinery, Syntaxin3 (Stx3). Surp

ePoster · Neuroscience

Topology-aware, unbiased grid coding for rapid task generalization

Heejun Kim, Nayeong Jeong, Sang Wan Lee · COSYNE 2025

The brain navigates mazes using the entorhinal grid and hippocampal place codes. While specific grid codes can represent maze structures independent of behavioral policy, little is known about how these task structure-specific grid codes generalize to novel tasks requiring different structures and policies. Reinforcement learning algorithms typically employ policy-biased representations, yet this approach lacks biological plausibility since it requires multiple distinct representations for task completion. To address these limitations, we propose topology-aware grid coding (TAG), a novel compu