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This Article Full Text Full Text (PDF) All Versions of this Article: 101/6/3031    most recent 91242.2008v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Web of Science (1) Google Scholar Articles by Pienkowski, M. Articles by Eggermont, J. J. PubMed PubMed Citation Articles by Pienkowski, M. Articles by Eggermont, J. J.

Wiener-Volterra Characterization of Neurons in Primary Auditory Cortex Using Poisson-Distributed Impulse Train Inputs

Departments of Physiology and Biophysics and Psychology, University of Calgary, Calgary, Alberta, Canada

Submitted 21 November 2008; accepted in final form 18 March 2009

An extension of the Wiener-Volterra theory to a Poisson-distributed impulse train input was used to characterize the temporal response properties of neurons in primary auditory cortex (AI) of the ketamine-anesthetized cat. Both first- and second-order "Poisson-Wiener" (PW) models were tested on their predictions of temporal modulation transfer functions (tMTFs), which were derived from extracellular spike responses to periodic click trains with click repetition rates of 2–64 Hz. Second-order (i.e., nonlinear) PW fits to the measured tMTFs could be described as very good in a majority of cases (e.g., predictability 80%) and were almost always superior to first-order (i.e., linear) fits. In all sampled neurons, second-order PW kernels showed strong compressive nonlinearities (i.e., a depression of the impulse response) but never expansive nonlinearities (i.e., a facilitation of the impulse response). In neurons with low-pass tMTFs, the depression decayed exponentially with the interstimulus lag, whereas in neurons with band-pass tMTFs, the depression was typically double-peaked, and the second peak occurred at a lag that correlated with the neuron's best modulation frequency. It appears that modulation-tuning in AI arises in part from an interplay of two nonlinear processes with distinct time courses.