Statistical models for neural encoding, decoding,
and optimal stimulus design
Chapter in
Computational Neuroscience: Progress in Brain Research,
eds. Cisek, P., Drew, T. & Kalaska, J.; pp. 493-507.
There are two basic problems in the statistical analysis of neural
data. The ``encoding'' problem concerns how information is encoded in
neural spike trains: can we predict the spike trains of a neuron (or
population of neurons), given an arbitrary stimulus or observed motor
response? Conversely, the ``decoding'' problem concerns how much
information is in a spike train: in particular, how well can we
estimate the stimulus that gave rise to the spike train?
This chapter describes statistical model-based techniques that in some
cases provide a unified solution to these two coding problems. These
models can capture stimulus dependencies as well as spike history and
interneuronal interaction effects in population spike trains, and are
intimately related to biophysically-based models of integrate-and-fire
type. We describe flexible, powerful likelihood-based methods for
fitting these encoding models and then for using the models to perform
optimal decoding. Each of these (apparently quite difficult) tasks
turn out to be highly computationally tractable, due to a key
concavity property of the model likelihood. Finally, we return to the
encoding problem to describe how to use these models to adaptively
optimize the stimuli presented to the cell on a trial-by-trial basis,
in order that we may infer the optimal model parameters as efficiently
as possible.
Preprint (400K,
pdf) | Liam Paninski's
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