Response variability in balanced cortical networks

Details Response variability in balanced cortical networks Response variability in balanced cortical networks

2004-02-10

arxiv.org/abs/q-bio/0402022v1

Biology

Quantitative Biology

Neurons and Cognition

22 pages, 7 figures, submitted to Neural Computation

Scientific paper

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, 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 post-firing 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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