Response variability in balanced cortical networks.
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.
Lerchner
Alexander
A
Technical University of Denmark, 2800 Lyngby, Denmark. LerchnerA@mail.nih.gov
Ursta
Cristina
C
Hertz
John
J
Ahmadi
Mandana
M
Ruffiot
Pauline
P
Enemark
Søren
S
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D002462:Cell Membrane / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology*; D005829:Geniculate Bodies / Q000502:physiology; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology; D014795:Visual Pathways / Q000502:physiology