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Neural Computation, Vol 9, 971-983, Copyright © 1997 by The MIT Press
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Todd W. Troyer and Kenneth D. Miller
To understand the interspike interval (ISI) variability displayed by visual
cortical neurons (Softky & Koch, 1993), it is critical to examine
the dynamics of their neuronal integration, as well as the variability in
their synaptic input current. Most previous models have focused on the
latter factor. We match a simple integrate-and-fire model to the
experimentally measured integrative properties of cortical regular spiking
cells (McCormick, Connors, Lighthall, & Prince, 1985). After
setting RC parameters, the post-spike voltage reset is set to match
experimental measurements of neuronal gain (obtained from in vitro plots of
firing frequency versus injected current). Examination of the resulting
model leads to an intuitive picture of neuronal integration that unifies
the seemingly contradictory 1= 
N and random
walk pictures that have previously been proposed. When ISIs are dominated
by postspike recovery, 1= N arguments hold
and spiking is regular; after the "memory" of the last spike becomes
negligible, spike threshold crossing is caused by input variance around a
steady state and spiking is Poisson. In integrate-and-fire neurons matched
to cortical cell physiology, steady-state behavior is predominant, and ISIs
are highly variable at all physiological firing rates and for a wide range
of inhibitory and excitatory inputs.
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