title: Bayesian model selection and minimum description length estimation of auditory-nerve discharge rates.
abstract: Auditory-nerve fiber discharges are modeled as self-exciting point processes with intensity given by the product of a stimulus-related function and a refractory-related function. Previous methods of estimating these two functions, based on the maximum-likelihood principle, have the problem of estimating more parameters than the data can support. A new procedure, based on a Bayes criterion for choosing the complexity of the model in addition to estimating the parameters, solves the over-parametrization problem. This procedure is seen to relate asymptotically to Rissanen's minimum description length (MDL) criterion. A performance comparison of the MDL procedure with previous maximum-likelihood algorithms promotes the adoption of the MDL procedure for simultaneous estimation of the stimulus and recovery properties of auditory-nerve discharge.
authors.affiliation: Department of Electrical Engineering, Washington University, St. Louis 63130.
authors.forename: K E
authors.initials: KE
authors.lastname: Mark
authors.forename: M I
authors.initials: MI
authors.lastname: Miller
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008959:Models, Neurological; D010898:Pitch Perception / Q000502:physiology; D011571:Psychoacoustics; D009435:Synaptic Transmission / Q000502:physiology*; D000159:Vestibulocochlear Nerve / Q000502:physiology*