1. Death and rebirth of neural activity in sparse inhibitory networks
- Author
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Simona Olmi, Alessandro Torcini, David Angulo-Garcia, Stefano Luccioli, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Institut de Neurobiologie de la Méditerranée [Aix-Marseille Université] (INMED - INSERM U901), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences des Systèmes (INS), Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, Consiglio Nazionale delle Ricerche, Istituto Nazionale di Fisica Nucleare, Sezione di Firenze (INFN, Sezione di Firenze), Istituto Nazionale di Fisica Nucleare (INFN), Laboratoire de Physique Théorique et Modélisation (LPTM - UMR 8089), Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Max-Planck-Institut für Physik komplexer Systeme (MPI-PKS), Max-Planck-Gesellschaft, and Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU)
- Subjects
leaky integrate-and-fire model ,neural network ,FOS: Physical sciences ,Striatum ,pulse-coupled neural models ,Inhibitory postsynaptic potential ,Medium spiny neuron ,01 natural sciences ,03 medical and health sciences ,Bursting ,Neural activity ,0302 clinical medicine ,0103 physical sciences ,[PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn] ,010306 general physics ,firing statistics ,Spiking neural network ,Physics ,Quantitative Biology::Neurons and Cognition ,Disordered Systems and Neural Networks (cond-mat.dis-nn) ,Condensed Matter - Disordered Systems and Neural Networks ,Nonlinear Sciences - Chaotic Dynamics ,Winner-take-all ,inhibition ,Living matter ,Information coding ,nervous system ,FOS: Biological sciences ,Quantitative Biology - Neurons and Cognition ,[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD] ,lyapunov analysis ,Neurons and Cognition (q-bio.NC) ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,Chaotic Dynamics (nlin.CD) ,Neuroscience ,030217 neurology & neurosurgery - Abstract
In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of the neural activity, as expected, but it can also promote neural reactivation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neurons' death). However, the random pruning of the connections is able to reverse the action of inhibition, i.e. in a sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of the neurons (neurons' rebirth). Thus the number of firing neurons reveals a minimum at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by the neurons with higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving an analytic mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, the system passes from a perfectly regular evolution to an irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum., 19 pages, 10 figures, submitted to NJP
- Published
- 2017
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