Searching for optimal stimuli: ascending a neuron's response function.

Journal of Computational Neuroscience
Melinda Evrithiki Koelling, Duane Q Nykamp

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.

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Citations

May 9, 2014·PLoS Computational Biology·Sven DähneLaurenz Wiskott
Aug 26, 2014·Journal of Computational Neuroscience·Joshua Chang, David Paydarfar
Aug 9, 2019·Journal of Neural Engineering·Charles-Henri VilaDaniel B Polley

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