Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

ABSTRACT: Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal s

SUBMITTER: Chang J 

PROVIDER: S-EPMC4225195 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature