Current Research
The neural coding problem is perhaps the fundamental question in systems neuroscience: given some input stimulus (or movement, or thought, etc.), what is the conditional probability of a neural response? The roadblock is that we want to know about these response probabilities given any possible input, and there are typically more such inputs than we can ever hope to sample. Thus the neural coding problem is fundamentally a statistics problem: given a finite number of samples of physiological data, how do we learn the neural codebook?
Below are more detailed descriptions of some (overlapping) themes in our recent neural coding research. These neural problems, in turn, have led to a number of problems which are interesting from a purely statistical point of view, with connections to machine learning, latent variable methods, high-dimensional regression, fast state-space smoothing methods, etc.

Likelihood-based neural data analysis: predicting and decoding spike trains

Estimation of information-theoretic quantities given limited data

Estimation and analysis of stochastic biophysical neural models

Optimal information-theoretic design of experiments

Population coding in primary motor cortex; neural prosthetic design

Neural coding in the retina

Some of the above research overviews are somewhat dated; a complete chronological list of our publications is available here.

Finally, see this page for some lecture notes and background on the methods we're working on.

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