The standard theory of a leaky integrator with stochastic spike input predicts that cortical cells should fire regularly. We tested interspike-interval histograms from awake, behaving macaque visual cortex (V1 and MT), and found high levels of variability (CV>0.5) characteristic of a nearly random (Poisson) process. A simple integrate-and-fire model, using accepted biophysical parameters, fails by more than a factor of ten to account for the high CV. We also simulated a biophysically detailed compartment-model of an anatomically reconstructed and physiologically characterized layer V pyramidal cell; again, at high firing rate, CV values are low, in disagreement with the data. Only a few situations could account for this discrepency: very large EPSP’s, a very short membrane time constant (τm<0.3 msec), highly synchronized input, or spikes in excitable dendrites. Our analysis suggests that cortical computation may occur at a time-scale much faster than previously realized.

When a typical nerve cell is injected with enough current, it fires a regular stream of action potentials. But cortical cells in vivo usually fire irregularly, reflecting synaptic input from presynaptic cells as well as intrinsic biophysical properties. We have applied the theory of stochastic \nprocesses to spike trains recorded from cortical neurons (Tuckwell 1989) and find a fundamental contradiction between the large interspike variability observed and the much lower values predicted by well-accepted biophysical models of single cells.