Characterizing neural gain control using spike-triggered covariance

O Schwartz, E J Chichilnisky, and E P Simoncelli

Presented at:
Neural Information Processing Systems (NIPS*01)

Published in Adv. Neural Information Processing Systems (NIPS*01), vol.14 pp. 269--276, May 2002.
© MIT Press, Cambridge, MA

Honorable mention, best student paper award.


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    • 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 salamander retinal ganglion cell data. Analysis of physiological data reveals meaningful suppressive axes and explains interesting nonlinearities. We expect this method to be applicable to other sensory areas and modalities.
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