title: A point process model for auditory neurons considering both their intrinsic dynamics and the spectrotemporal properties of an extrinsic signal.
abstract: 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 spectrotemporal 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 spectrotemporal 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.
authors.affiliation: Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. eplourde@mit.edu
authors.forename: Eric
authors.initials: E
authors.lastname: Plourde
authors.forename: Bertrand
authors.initials: B
authors.lastname: Delgutte
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D002415:Cats; D003056:Cochlear Nerve / Q000166:cytology / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D012044:Regression Analysis