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oai:recercat.cat:2072/5370512024-12-20T01:33:37Zcom_2072_199267com_2072_4427col_2072_201036

Rapid memory encoding in a recurrent network model with behavioral time scale synaptic plasticity Li, P.Y. Roxin, A. Episodic memories,correlation of the synaptic weight matrix with any given past environment, Calculated memory trace Episodic memories are formed after a single exposure to novel stimuli. The plasticity mechanisms underlying such fast learning still remain largely unknown. Recently, it was shown that cells in area CA1 of the hippocampus of mice could form or shift their place fields after a single traversal of a virtual linear track. In-vivo intracellular recordings in CA1 cells revealed that previously silent inputs from CA3 could be switched on when they occurred within a few seconds of a dendritic plateau potential (PP) in the post-synaptic cell, a phenomenon dubbed Behavioral Time-scale Plasticity (BTSP). A recently developed computational framework for BTSP in which the dynamics of synaptic traces related to the pre-synaptic activity and post-synaptic PP are explicitly modelled, can account for experimental findings. Here we show that this model of plasticity can be further simplified to a 1D map which describes changes to the synaptic weights after a single trial. We use a temporally symmetric version of this map to study the storage of a large number of spatial memories in a recurrent network, such as CA3. Specifically, the simplicity of the map allows us to calculate the correlation of the synaptic weight matrix with any given past environment analytically. We show that the calculated memory trace can be used to predict the emergence and stability of bump attractors in a high dimensional neural network model endowed with BTSP. Copyright: © 2023 Li, Roxin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This work was funded by a grant from theSpanish Ministry of Science and InnovationPID2021-124702OB-I00 (AR). This work issupported by the Spanish State Research Agency,through the Severo Ochoa and Maria de Maeztuprogram for Centers and Units of Excellence inR&D (CEX2020- 001084-M) (AR). We thankCERCA Program/Generalitat de Catalunya forinstitutional support. The funders had no role instudy design, data collection and analysis, decisionto publish, or preparation of the manuscript. 2023-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/2072/537051 10.1371/journal.pcbi.1011139 eng PLoS Computational Biology L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: https://creativecommons.org/licenses/by/4.0/ L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess 31 p. application/pdf Public Library of Science RECERCAT (Dipòsit de la Recerca de Catalunya)