288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

December 1, 2004, Vol. 16, No. 12, Pages 2533-2561
(doi: 10.1162/0899766042321797)
© 2004 Massachusetts Institute of Technology
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
Article PDF (548.08 KB)

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.