title: A parameter-space search algorithm tested on a Hodgkin-Huxley model.
abstract: We demonstrate a parameter-space search algorithm using a computational model of a single-compartment neuron with conductance-based Hodgkin-Huxley dynamics. To classify bursting (the desired behavior), we use a simple cost function whose inputs are derived from the frequency content of the neural output. Our method involves the repeated use of a stochastic gradient descent-type algorithm to locate parameter values that allow the neural model to produce bursting within a specified tolerance. We demonstrate good results, including those showing that the utility of our algorithm improves as the pre-defined allowable parameter ranges increase and that the initial approach to our method is computationally efficient.
authors.affiliation: Laboratory for Neuroengineering, Georgia Institute of Technology, 313 Ferst Drive, Atlanta, GA 30332, USA.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Reid
authors.forename: Edgar A
authors.initials: EA
authors.lastname: Brown
authors.forename: Stephen P
authors.initials: SP
authors.lastname: DeWeerth
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D005583:Fourier Analysis; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013997:Time Factors