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

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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.

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