A Topological Model of the Hippocampal Cell Assembly Network

Overview

Journal Front Comput Neurosci

Specialty Biology

Date 2016 Jun 18

PMID 27313527

Citations 11

Authors

Andrey Babichev

Daoyun Ji

Facundo Memoli

Yuri A Dabaghian

Affiliations

Soon will be listed here.

Abstract

It is widely accepted that the hippocampal place cells' spiking activity produces a cognitive map of space. However, many details of this representation's physiological mechanism remain unknown. For example, it is believed that the place cells exhibiting frequent coactivity form functionally interconnected groups-place cell assemblies-that drive readout neurons in the downstream networks. However, the sheer number of coactive combinations is extremely large, which implies that only a small fraction of them actually gives rise to cell assemblies. The physiological processes responsible for selecting the winning combinations are highly complex and are usually modeled via detailed synaptic and structural plasticity mechanisms. Here we propose an alternative approach that allows modeling the cell assembly network directly, based on a small number of phenomenological selection rules. We then demonstrate that the selected population of place cell assemblies correctly encodes the topology of the environment in biologically plausible time, and may serve as a schematic model of the hippocampal network.

Citing Articles

Spaces and sequences in the hippocampus: a homological perspective.

Babichev A, Vashin V, Dabaghian Y bioRxiv. 2025; .

PMID: 39975300 PMC: 11839069. DOI: 10.1101/2025.02.08.637255.

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields.

Levy E, Carrillo-Segura S, Park E, Redman W, Hurtado J, Chung S Cell Rep. 2023; 42(10):113142.

PMID: 37742193 PMC: 10842170. DOI: 10.1016/j.celrep.2023.113142.

An application of neighbourhoods in digraphs to the classification of binary dynamics.

Conceicao P, Govc D, Lazovskis J, Levi R, Riihimaki H, Smith J Netw Neurosci. 2022; 6(2):528-551.

PMID: 35733429 PMC: 9208003. DOI: 10.1162/netn_a_00228.

Geodesic-based distance reveals nonlinear topological features in neural activity from mouse visual cortex.

Beshkov K, Tiesinga P Biol Cybern. 2021; 116(1):53-68.

PMID: 34816322 DOI: 10.1007/s00422-021-00906-5.

Spatial representability of neuronal activity.

Akhtiamov D, Cohn A, Dabaghian Y Sci Rep. 2021; 11(1):20957.

PMID: 34697340 PMC: 8546096. DOI: 10.1038/s41598-021-00281-y.

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