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Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model (2004)

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by Liam Paninski , Jonathan W. Pillow , Eero P. Simoncelli
Citations:83 - 24 self
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BibTeX

@MISC{Paninski04maximumlikelihood,
    author = {Liam Paninski and Jonathan W. Pillow and Eero P. Simoncelli},
    title = {Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model},
    year = {2004}
}

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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.

Keyphrases

maximum likelihood estimation    stochastic integrate-and-fire neural model    unique global maximum    numerical simulation    extracellular spike train response    integrate-and-fire spike generation mechanism    neural response    realistic alternative    maximum likelihood estimator    model validity    density evolution technique    spike generation    linear filtering stage    maximum likelihood solution    efficient algorithm    log likelihood function    model parameter    intracellular voltage data    gradient ascent technique   

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