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Scientific article
English

Transient information flow in a network of excitatory and inhibitory model neurons: Role of noise and signal autocorrelation

Published inJournal of Physiology-Paris, vol. 98, no. 4-6, p. 417-428
Publication date2004
Abstract

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation of the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.

Keywords
  • Recurrent integrate-and-fire neuron networks
  • Sparse connectivity
  • Population dynamics
  • Information processing
Affiliation Not a UNIGE publication
Research group
Citation (ISO format)
MAYOR, Julien, GERSTNER, Wulfram. Transient information flow in a network of excitatory and inhibitory model neurons: Role of noise and signal autocorrelation. In: Journal of Physiology-Paris, 2004, vol. 98, n° 4-6, p. 417–428. doi: 10.1016/j.jphysparis.2005.09.009
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