title: Searching for optimal stimuli: ascending a neuron's response function.
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.
authors.affiliation: Western Michigan University, Kalamazoo, MI, USA. melinda.koelling@wmich.edu
authors.forename: Melinda Evrithiki
authors.initials: ME
authors.lastname: Koelling
authors.forename: Duane Q
authors.initials: DQ
authors.lastname: Nykamp
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D055724:Electrophysiological Phenomena / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D009146:Music / Q000523:psychology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D010775:Photic Stimulation