Analytical model for the effects of learning on spike count distributions

The spike count distribution observed when recording from a variety of neurons in many different conditions has a fairly stereotypical shape, with a single mode at zero or close to a low average count, and a long, quasi-exponential tail to high counts. Such a distribution has been suggested to be the direct result of three simple facts: the firing frequency of a typical cortical neuron is close to linear in the summed input current entering the soma, above a threshold; the input current varies on several timescales, both faster and slower than the window used to count spikes; and the input distribution at any timescale can be taken to be approximately normal. The third assumption is violated by associative learning, which generates correlations between the synaptic weight vector on the dendritic tree of a neuron, and the input activity vectors it is repeatedly subject to. We show analytically that for a simple feed-forward model, the normal distribution of the slow components of the input current becomes the sum of two quasi-normal terms. The term important below threshold shifts with learning, while the term important above threshold does not shift but grows in width. These deviations from the standard distribution may be observable in appropriate recording experiments.