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We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron’s intrinsic dynamics as well as the spectro-temporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron’s baseline spike rate, its intrinsic dynamics - spiking history - and the stimulus effect which in this case is the analog of the spectro-temporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron’s intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF.

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