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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1432593
This article is part of the Research Topic Hippocampal Function and Reinforcement Learning View all articles

Multiscale modelling of neuronal dynamics in hippocampus CA1

Provisionally accepted
Federico Tesler Federico Tesler 1*Roberta M. Lorenzi Roberta M. Lorenzi 2Adam Ponzi Adam Ponzi 3Claudia Casellato Claudia Casellato 2Fulvia Palesi Fulvia Palesi 2Daniela Gandolfi Daniela Gandolfi 4Claudia A. Gandini Wheeler-Kingshott Claudia A. Gandini Wheeler-Kingshott 2Jonathan Mapelli Jonathan Mapelli 4Egidio D‘Angelo Egidio D‘Angelo 2Michele Migliore Michele Migliore 3Alain Destexhe Alain Destexhe 1
  • 1 UMR9197 Institut des Neurosciences Paris Saclay (Neuro-PSI), Gif-sur-Yvette, France
  • 2 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Lombardy, Italy
  • 3 Institute of Biophysics, Palermo, Italy
  • 4 Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Emilia-Romagna, Italy

The final, formatted version of the article will be published soon.

    The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area.

    Keywords: Spiking Neural network, Hippocampus, mean-field, traveling waves, oscillations

    Received: 14 May 2024; Accepted: 17 Jul 2024.

    Copyright: © 2024 Tesler, Lorenzi, Ponzi, Casellato, Palesi, Gandolfi, Gandini Wheeler-Kingshott, Mapelli, D‘Angelo, Migliore and Destexhe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Federico Tesler, UMR9197 Institut des Neurosciences Paris Saclay (Neuro-PSI), Gif-sur-Yvette, France

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