Dimensionality reduction in neural models: An
information-theoretic generalization of spike triggered
average and covariance analysis
Published in:
Journal of Vision
6(4):414-428, May 2006.
Related articles:
Companion paper on Spike-triggered average and covariance methods:
JOV-06
We describe an information-theoretic framework for fitting neural spike responses
with a Linear-Nonlinear-Poisson cascade model. This framework unifies
the spike-triggered average and spike-triggered covariance approaches to neural
characterization, and recovers a set of linear filters that maximize mean and
variance-dependent information between stimuli and spike responses. The resulting
approach has several useful properties: (1) it recovers a set of linear
filters sorted according to their informativeness about the neural response; (2)
it is both computationally efficient and robust, allowing recovery of multiple linear
filters from a data set of relatively modest size; (3) it provides an explicit
default model of the nonlinear stage mapping the filter responses to spike rate,
in the form of a ratio of Gaussians. (4) it is equivalent to maximum likelihood
estimation of this default model, but also converges to the correct filter estimates
whenever the conditions for the consistency of spike-triggered average or covariance
analysis are met; (5) it can be augmented with additional constraints, such
as space-time separability, on the filters. We demonstrate the effectiveness of
the method by applying it to simulated responses of a Hodgkin-Huxley neuron,
and the recorded extracellular responses of macaque retinal ganglion cells and
V1 cells.
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