Characterizing Neural Gain Control using Spike-triggered Covariance

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Odelia Schwartz, E.J. Chichilnisky, Eero Simoncelli


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 suppressive 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 significant suppressive axes and explains neural nonlinearities. This method should be applicable to other sensory areas and modalities.