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

Melinda Evrithiki Koelling, Duane Q. Nykamp

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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 languageEnglish (US)
Pages (from-to)449-473
Number of pages25
JournalJournal of Computational Neuroscience
Volume33
Issue number3
DOIs
StatePublished - Dec 1 2012

Keywords

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

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