Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model

Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli

Research output: Contribution to journalReview article

Abstract

We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrate-and-fire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors seen in vivo. We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). Specifically, we prove that the log-likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques. We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model's validity using time-rescaling and density evolution techniques.

Original languageEnglish (US)
Pages (from-to)2533-2561
Number of pages29
JournalNeural computation
Volume16
Issue number12
DOIs
StatePublished - Dec 2004

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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