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Computing linear approximations to nonlinear neuronal responses

BMC Neuroscience20089(Suppl 1):P118

Published: 11 July 2008


Animal ModelLinear ModelLinear RelationshipGeneralize Linear ModelNeuronal Activity

Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. For example, analyses based on spike-triggered average or generalized linear models (GLMs) assume that the only nonlinearity is the spiking nonlinearity, e.g. a threshold. However, many neurons have a response pattern that exhibits a more fundamental nonlinearity. For example, the nonlinearity of a neuron which is highly selective to a small class of images or songs may not be captured by a GLM because such selectivity implies strong sensitivity to multiple directions in stimulus space. Nonetheless, the response of such a neuron can be captured by a linear model if the stimulus is constrained to be close to some stimulus of interest, and the local linear approximation gives insight into neuronal behavior near that stimulus. We derive a modification of the spike-triggered average to compute such local linear approximations and demonstrate via simulation how they can reveal hidden features of the neuron's response.

Authors’ Affiliations

Department of Mathematics, Western Michigan University, Kalamazoo, USA
School of Mathematics, University of Minnesota, Minneapolis, USA


© Koelling and Nykamp; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.