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This article is part of the supplement: Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

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Computing linear approximations to nonlinear neuronal responses

Melinda E Koelling1 and Duane Q Nykamp2*

Author Affiliations

1 Department of Mathematics, Western Michigan University, Kalamazoo, MI 49008, USA

2 School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA

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BMC Neuroscience 2008, 9(Suppl 1):P118  doi:10.1186/1471-2202-9-S1-P118

The electronic version of this article is the complete one and can be found online at:

Published:11 July 2008

© 2008 Koelling and Nykamp; licensee BioMed Central Ltd.

Poster presentation

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.