The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of 'stable chaos,' i.e., by observing that the stochasticlike behavior is 'limited' to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary.

Desynchronization in diluted neural networks

Livi, R.;Politi, A.;Torcini, A.
2006

Abstract

The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of 'stable chaos,' i.e., by observing that the stochasticlike behavior is 'limited' to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary.
Campo DC Valore Lingua
dc.authority.ancejournal PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS en
dc.authority.orgunit Istituto dei Sistemi Complessi - ISC en
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dc.authority.people Zillmer, R. en
dc.authority.people Livi, R. en
dc.authority.people Politi, A. en
dc.authority.people Torcini, A. en
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dc.description.abstracteng The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of 'stable chaos,' i.e., by observing that the stochasticlike behavior is 'limited' to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary. -
dc.description.affiliations 1) INFN Sezione Firenze, via Sansone 1, I-50019 Sesto Fiorentino, Italy 2) Dipartimento di Fisica, Universitá di Firenze, via Sansone 1, I-50019 Sesto Fiorentino, Italy 3) Sezione INFN, Unita’ INFM e Centro Interdipartimentale per lo Studio delle Dinamiche Complesse, via Sansone 1, I-50019 Sesto Fiorentino, Italy 4) Istituto dei Sistemi Complessi, CNR, CNR, via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy 5) Centro Interdipartimentale per lo Studio delle Dinamiche Complesse, via Sansone 1, I-50019 Sesto Fiorentino -
dc.description.allpeople Zillmer, R.; Livi, R.; Politi, A.; Torcini, A. -
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dc.identifier.doi 10.1103/PhysRevE.74.036203 en
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dc.title Desynchronization in diluted neural networks en
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