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Abstract:
Spike-triggered averaging techniques are effective for linear
characterization of neural responses. But neurons exhibit
important nonlinear behaviors, such as gain control, that are not
captured by such analyses. We describe a spike-triggered
covariance method for retrieving supressive components of the
gain control signal in a neuron. We demonstrate the method in
simulation and on retinal ganglion cell data. Analysis of
physiological data reveals suppressive axes and explains neural
nonlinearities. This method should be applicable to other sensory
areas and modalities.
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