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Response Variability in Balanced Cortical Networks

LerchnerA{at}mail.nih.gov Technical University of Denmark, 2800 Lyngby, Denmark

Cristina Ursta

cristina{at}nordita.dk Niels Bohr Institut, 2100 Copenhagen Ø, Denmark

John Hertz

hertz{at}nordita.dk Nordita, 2100 Copenhagen Ø, Denmark

Mandana Ahmadi

ahmadi{at}nordita.dk Nordita, 2100 Copenhagen Ø, Denmark

Pauline Ruffiot

pruffiot{at}ujf-grenoble.fr Université Joseph Fourier, Grenoble, France

Søren Enemark

enemark{at}nbi.dk Niels Bohr Institut, 2100 Copenhagen Ø, Denmark

We study the spike statistics of neurons in a network with dynamically balanced excitation and inhibition. Our model, intended to represent a generic cortical column, comprises randomly connected excitatory and inhibitory leaky integrate-and-fire neurons, driven by excitatory input from an external population. The high connectivity permits a mean field description in which synaptic currents can be treated as gaussian noise, the mean and autocorrelation function of which are calculated self-consistently from the firing statistics of single model neurons. Within this description, a wide range of Fano factors is possible. We find that the irregularity of spike trains is controlled mainly by the strength of the synapses relative to the difference between the firing threshold and the postfiring reset level of the membrane potential. For moderately strong synapses, we find spike statistics very similar to those observed in primary visual cortex.

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