Searching for optimal stimuli: Ascending a neuron's response function

Melinda Evrithiki Koelling; Duane Q. Nykamp

doi:10.1007/s10827-012-0395-7

Searching for optimal stimuli: Ascending a neuron's response function

Melinda Evrithiki Koelling, Duane Q. Nykamp

Mathematics

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Abstract

Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.

Original language	English (US)
Pages (from-to)	449-473
Number of pages	25
Journal	Journal of Computational Neuroscience
Volume	33
Issue number	3
DOIs	https://doi.org/10.1007/s10827-012-0395-7
State	Published - Dec 2012

Bibliographical note

Funding Information:
Acknowledgements We thank Teresa Nick for inspiring this line of research, Greg Horwitz for helpful discussions, and the anonymous reviewers for their suggestions. This research was supported by the National Science Foundation grant DMS-0719724 (DQN).

Keywords

Closed loop experiment
Gradient ascent
Mean firing rate maximization
Reverse correlation
Spike-triggered average

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title = "Searching for optimal stimuli: Ascending a neuron's response function",

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KW - Reverse correlation

KW - Spike-triggered average

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Searching for optimal stimuli: Ascending a neuron's response function

Abstract

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Keywords

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