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The Journal of Neuroscience, August 15, 2000, 20(16):6193-6209
Impact of Correlated Synaptic Input on Output Firing Rate and
Variability in Simple Neuronal Models
Emilio
Salinas1 and
Terrence J.
Sejnowski2
1 Computational Neurobiology Laboratory,
Howard Hughes Medical Institute, The Salk Institute for Biological
Studies, La Jolla, California 92037, and
2 Department of Biology, University of California
at San Diego, La Jolla, California 92093
Cortical neurons are typically driven by thousands of synaptic
inputs. The arrival of a spike from one input may or may not be
correlated with the arrival of other spikes from different inputs. How
does this interdependence alter the probability that the postsynaptic
neuron will fire? We constructed a simple random walk model in which
the membrane potential of a target neuron fluctuates stochastically,
driven by excitatory and inhibitory spikes arriving at random times. An
analytic expression was derived for the mean output firing rate as a
function of the firing rates and pairwise correlations of the inputs.
This stochastic model made three quantitative predictions. (1)
Correlations between pairs of excitatory or inhibitory inputs increase
the fluctuations in synaptic drive, whereas correlations between
excitatory-inhibitory pairs decrease them. (2) When excitation and
inhibition are fully balanced (the mean net synaptic drive is zero),
firing is caused by the fluctuations only. (3) In the balanced case,
firing is irregular. These theoretical predictions were in excellent
agreement with simulations of an integrate-and-fire neuron that
included multiple conductances and received hundreds of synaptic
inputs. The results show that, in the balanced regime, weak
correlations caused by signals shared among inputs may have a
multiplicative effect on the input-output rate curve of a postsynaptic
neuron, i.e. they may regulate its gain; in the unbalanced regime,
correlations may increase firing probability mainly around threshold,
when output rate is low; and in all cases correlations are expected to
increase the variability of the output spike train.
Key words:
random-walk; integrate-and-fire; computer simulation; spike synchrony; oscillations; cross-correlation; balanced inhibition; cerebral cortex
Copyright © 2000 Society for Neuroscience 0270-6474/00/20166193-17$05.00/0
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