Maximum likelihood estimation of cascade point-process neural encoding models.

Liam Paninski

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PubMed · 15600233

Maximum likelihood estimation of cascade point-process neural encoding models.

Liam Paninski

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Abstract

Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a known parametric function; the assumption that this function is known speeds the estimation process considerably. We investigate the shape of the likelihood function for this type of model, give a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood-leading, in turn, to efficient algorithms for the computation of the maximum likelihood estimator-and discuss the implications for the form of the allowed nonlinearities. Finally, we note some interesting connections between the likelihood-based estimators and the classical spike-triggered average estimator, discuss some useful extensions of the basic model structure, and provide two novel applications to physiological data.

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Algorithms Animals Likelihood Functions Models, Neurological Motor Cortex Neural Networks, Computer Neurons Journal Article Research Support, Non-U.S. Gov't

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Liam Paninski. 2004. Maximum likelihood estimation of cascade point-process neural encoding models.. https://pubmed.ncbi.nlm.nih.gov/15600233/

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