Neural Comput - Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

Tópicos

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Resumo

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Resumo Limpo

one central problem system neurosci understand neural spike train convey sensori inform decod method provid explicit mean read inform contain neural spike respons offer power set tool studi neural code problem develop sever decod method base pointprocess neural encod model forward model predict spike respons stimuli model concav loglikelihood function allow effici maximumlikelihood model fit stimulus decod present sever applic encod model framework problem decod stimulus inform popul spike respons tractabl algorithm comput maximum posteriori map estim stimulus probabl stimulus generat observ singl multipleneuron spike train respons given prior distribut stimulus gaussian approxim posterior stimulus distribut can use quantifi fidel various stimulus featur encod effici method estim mutual inform stimulus spike train emit neural popul framework detect changepoint time time stimulus undergo chang mean varianc margin posterior stimulus distribut provid sever exampl illustr perform estim simul real neural data

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