Modelbased decoding, information estimation, and changepoint detection techniques for multineuron spike trainsJonathan W. Pillow, Yashar Ahmadian, & Liam Paninski 
Neural Computation 23:145. (2011)
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 pointprocess neural encoding models, or "forward" models that predict spike responses to stimuli. These models have concave loglikelihood functions, which allow for efficient maximumlikelihood model fitting and stimulus decoding. We present several applications of the encodingmodel 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 multipleneuron 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 changepoint times (e.g., 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. 
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