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A neuron model of stochastic resonance using rectangular pulse trains.

Publication ,  Journal Article
Danziger, Z; Grill, WM
Published in: Journal of computational neuroscience
February 2015

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.

Duke Scholars

Published In

Journal of computational neuroscience

DOI

EISSN

1573-6873

ISSN

0929-5313

Publication Date

February 2015

Volume

38

Issue

1

Start / End Page

53 / 66

Related Subject Headings

  • Stochastic Processes
  • Neurons
  • Neurology & Neurosurgery
  • Nerve Net
  • Models, Neurological
  • Humans
  • Animals
  • Action Potentials
  • 52 Psychology
  • 46 Information and computing sciences
 

Citation

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Danziger, Z., & Grill, W. M. (2015). A neuron model of stochastic resonance using rectangular pulse trains. Journal of Computational Neuroscience, 38(1), 53–66. https://doi.org/10.1007/s10827-014-0526-4
Danziger, Zachary, and Warren M. Grill. “A neuron model of stochastic resonance using rectangular pulse trains.Journal of Computational Neuroscience 38, no. 1 (February 2015): 53–66. https://doi.org/10.1007/s10827-014-0526-4.
Danziger Z, Grill WM. A neuron model of stochastic resonance using rectangular pulse trains. Journal of computational neuroscience. 2015 Feb;38(1):53–66.
Danziger, Zachary, and Warren M. Grill. “A neuron model of stochastic resonance using rectangular pulse trains.Journal of Computational Neuroscience, vol. 38, no. 1, Feb. 2015, pp. 53–66. Epmc, doi:10.1007/s10827-014-0526-4.
Danziger Z, Grill WM. A neuron model of stochastic resonance using rectangular pulse trains. Journal of computational neuroscience. 2015 Feb;38(1):53–66.
Journal cover image

Published In

Journal of computational neuroscience

DOI

EISSN

1573-6873

ISSN

0929-5313

Publication Date

February 2015

Volume

38

Issue

1

Start / End Page

53 / 66

Related Subject Headings

  • Stochastic Processes
  • Neurons
  • Neurology & Neurosurgery
  • Nerve Net
  • Models, Neurological
  • Humans
  • Animals
  • Action Potentials
  • 52 Psychology
  • 46 Information and computing sciences