Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation.
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.
Mayor
Julien
J
School of Computer and Communication Sciences and Brain-Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Switzerland. julien.mayor@epfl.ch
Gerstner
Wulfram
W
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology; D008433:Mathematics; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D009433:Neural Inhibition; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes; D013997:Time Factors