P283 A biophysical model of CA1 pyramidal cell heterogeneity in memory stability and flexible decision-making

Fei Song*1,2, Bailu Si3,4


1State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Science, Shenyang, Liaoning, China
2University of Chinese Academy of Sciences, Beijing, Beijing, China
3School of Systems Sciences, Beijing Normal University, Beijing, Beijing, China
4Chinese Institute for Brain Research, Beijing, Beijing, China


*Email: songfeo20160903@gmail.com
Introduction

The entorhinal-hippocampal system is essential for spatial memory and navigation. CA1 integrates spatial and non-spatial inputs via two pathways: the perforant path (PP) and temporoammonic path (TA), processed by pyramidal cells (PCs) [1]. We propose a biophysical model with simple PCs and complex PCs [2]. Simulations in novel environments (Fig. 1a) show sPCs maintain stable spatial coding, while cPCs integrate spatial and attentional inputs, supporting decision-making. In familiar settings (Fig. 1b), cPCs adapt to changes while sPCs preserve stable encoding, enabling memory retention and comparison of past and new experiences. This model unifies CA1’s roles in memory and decision-making.

Methods
We model CA1 as a two-layer network: deep-layer sPCs receive MEC input, while superficial-layer cPCs integrate MEC and LEC signals. Synaptic plasticity follows Hebbian learning, with SC weights adapting via dendritic-somatic co-activation and TA weights via rate-dependent learning, constrained by proximal-distal gradients. Simulations include a 10m track and 5m open-field, where MEC provides grid-cell input, LEC encodes egocentric cues, and CA3 supplies place-cell activity [3,4]. Memory recovery is evaluated via place field stability (JS distance), while stimulus-specific information quantifies spatial and attentional encoding variability [5]. A population decoder (MLP) predicts location and attention from CA1 activity.
Results
CA1 supports flexible decision-making by integrating spatial and perceptual information. In novel environments, sPCs ensure spatial stability, while cPCs encode stimulus-specific cues. A proximal-distal gradient in cPCs appears with fixed cues but disappears with moving cues, confirming their adaptive role. Population decoding shows cPCs excel in attention tracking, while sPCs maintain spatial coding. CA1 also aids memory updating. When CA3 recall is incomplete, CA1 preserves past memories longer than expected, slowing decay. When TA introduces novelty, cPCs encode new inputs while sPCs retain old ones, enabling stable yet adaptive memory processing. This mirrors real-world experiences, such as recognizing familiar but altered locations.
Discussion
Our model captures CA1 neuron heterogeneity and projection preferences in decision-making and memory updating. However, it simplifies CA3’s proximodistal heterogeneity, where pattern separation (proximal) and completion (distal) may influence CA1 dynamics [6]. Future work should refine CA3 input representation. CA1’s dual-pathway structure aligns with cognitive map theory, where novel environments require integration, while familiar ones involve consolidation. This parallels the Tolman-Eichenbaum Machine (TEM) model of hippocampal function [7]. The dual-pathway structure may reflect a generalized neuronal computation mechanism, extending beyond navigation and memory to broader cognitive functions.




Figure 1. Fig. 1 Functional Framework of the Hippocampus. (a) CA1 supports flexible decision-making in novel environments by integrating sensory inputs and generating context-specific representations. (b) CA1 facilitates memory updating in familiar environments by comparing stored memories with current experiences.
Acknowledgements
Not applicable.
References
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