Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloCoherent periodic activity in excitatory Erdös-Renyi neural networks: The role of network connectivity
Anno di pubblicazione2012
Formato
  • Elettronico
  • Cartaceo
Autore/iLorenzo Tattini (1); Simona Olmi (1,2); Alessandro Torcini (1,2)
Affiliazioni autori1) CNR--Consiglio Nazionale delle Ricerche--Istituto dei Sistemi Complessi, via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy 2) INFN--Sezione di Firenze and CSDC, via Sansone 1, 50019 Sesto Fiorentino, Italy
Autori CNR e affiliazioni
  • SIMONA OLMI
  • ALESSANDRO TORCINI
Lingua/e
  • inglese
AbstractIn this article, we investigate the role of connectivity in promoting coherent activity in excitatory neural networks. In particular, we would like to understand if the onset of collective oscillations can be related to a minimal average connectivity and how this critical connectivity depends on the number of neurons in the networks. For these purposes, we consider an excitatory random network of leaky integrate-and-fire pulse coupled neurons. The neurons are connected as in a directed Erdos-Renyi graph with average connectivity < k > scaling as a power law with the number of neurons in the network. The scaling is controlled by a parameter gamma, which allows to pass from massively connected to sparse networks and therefore to modify the topology of the system. At a macroscopic level, we observe two distinct dynamical phases: an asynchronous state corresponding to a desynchronized dynamics of the neurons and a regime of partial synchronization (PS) associated with a coherent periodic activity of the network. At low connectivity, the system is in an asynchronous state, while PS emerges above a certain critical average connectivity < k >(c). For sufficiently large networks, < k >(c) saturates to a constant value suggesting that a minimal average connectivity is sufficient to observe coherent activity in systems of any size irrespectively of the kind of considered network: sparse or massively connected. However, this value depends on the nature of the synapses: reliable or unreliable. For unreliable synapses, the critical value required to observe the onset of macroscopic behaviors is noticeably smaller than for reliable synaptic transmission. Due to the disorder present in the system, for finite number of neurons we have inhomogeneities in the neuronal behaviors, inducing a weak form of chaos, which vanishes in the thermodynamic limit. In such a limit, the disordered systems exhibit regular (non chaotic) dynamics and their properties correspond to that of a homogeneous fully connected network for any gamma-value. Apart for the peculiar exception of sparse networks, which remain intrinsically inhomogeneous at any system size.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da023133
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RivistaChaos (Clayton)
Attiva dal 1961 al 1964
Editore: Union Council, Monash University - Clayton, Vic.
Paese di pubblicazione: Australia
Lingua: inglese
ISSN: 1321-8611
Titolo chiave: Chaos (Clayton)
Titolo proprio: Chaos. (Clayton)
Numero volume della rivista22
Fascicolo della rivista2
DOI10.1063/1.4723839
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000305833900033)
  • Scopus (Codice:2-s2.0-84863492180)
Parole chiavechaos, complex networks, neural nets, random processes, synchronisation
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Strutture CNR
  • ISC — ISC - Sede secondaria di Firenze - Sesto Fiorentino
Moduli/Attività/Sottoprogetti CNR
  • MD.P02.017.001 : comportamento dinamico di sistemi complessi
Progetti Europei-
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Articolo pubblicato (documento privato )
Tipo documento: application/download