Bayesian model selection and minimum description length estimation of auditory-nerve discharge rates.

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.