A neuron model of stochastic resonance using rectangular pulse trains.

Journal Article (Journal Article)

Stochastic resonance (SR) is the enhanced representation of a weak input signal by the addition of an optimal level of broadband noise to a nonlinear (threshold) system. Since its discovery in the 1980s the domain of input signals shown to be applicable to SR has greatly expanded, from strictly periodic inputs to now nearly any aperiodic forcing function. The perturbations (noise) used to generate SR have also expanded, from white noise to now colored noise or vibrational forcing. This study demonstrates that a new class of perturbations can achieve SR, namely, series of stochastically generated biphasic pulse trains. Using these pulse trains as 'noise' we show that a Hodgkin Huxley model neuron exhibits SR behavior when detecting weak input signals. This result is of particular interest to neuroscience because nearly all artificial neural stimulation is implemented with square current or voltage pulses rather than broadband noise, and this new method may facilitate the translation of the performance gains achievable through SR to neural prosthetics.

Full Text

Duke Authors

Cited Authors

  • Danziger, Z; Grill, WM

Published Date

  • February 2015

Published In

Volume / Issue

  • 38 / 1

Start / End Page

  • 53 - 66

PubMed ID

  • 25186655

Pubmed Central ID

  • PMC4297574

Electronic International Standard Serial Number (EISSN)

  • 1573-6873

International Standard Serial Number (ISSN)

  • 0929-5313

Digital Object Identifier (DOI)

  • 10.1007/s10827-014-0526-4


  • eng