## Model-based decoding, information estimation, and
change-point detection techniques for multi-neuron spike trains

In press, Neural Computation

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 for 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 (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.

Reprint | Liam Paninski's research page