Abstract

We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment.

Original languageEnglish (US)
Pages (from-to)588-598
Number of pages11
JournalChaos
Volume8
Issue number3
DOIs
StatePublished - Jan 1 1998

Fingerprint

Neuronal Network
Stochastic Resonance
Electric fields
Electric Field
Brain
Signal to noise ratio
Physics
electric fields
Random Noise
random noise
Computational Model
brain
signal to noise ratios
Decrease
physics
optimization
Optimization
Experiments
Interaction
Experiment

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics

Cite this

Gluckman, Bruce ; So, Paul ; Netoff, Theoden I. ; Spano, Mark L. ; Schiff, Steven. / Stochastic resonance in mammalian neuronal networks. In: Chaos. 1998 ; Vol. 8, No. 3. pp. 588-598.
@article{4ae21a6c3f1d4de48670e7c27dfd9f62,
title = "Stochastic resonance in mammalian neuronal networks",
abstract = "We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment.",
author = "Bruce Gluckman and Paul So and Netoff, {Theoden I.} and Spano, {Mark L.} and Steven Schiff",
year = "1998",
month = "1",
day = "1",
doi = "10.1063/1.166340",
language = "English (US)",
volume = "8",
pages = "588--598",
journal = "Chaos",
issn = "1054-1500",
publisher = "American Institute of Physics Publising LLC",
number = "3",

}

Stochastic resonance in mammalian neuronal networks. / Gluckman, Bruce; So, Paul; Netoff, Theoden I.; Spano, Mark L.; Schiff, Steven.

In: Chaos, Vol. 8, No. 3, 01.01.1998, p. 588-598.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Stochastic resonance in mammalian neuronal networks

AU - Gluckman, Bruce

AU - So, Paul

AU - Netoff, Theoden I.

AU - Spano, Mark L.

AU - Schiff, Steven

PY - 1998/1/1

Y1 - 1998/1/1

N2 - We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment.

AB - We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment.

UR - http://www.scopus.com/inward/record.url?scp=0007422443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0007422443&partnerID=8YFLogxK

U2 - 10.1063/1.166340

DO - 10.1063/1.166340

M3 - Article

AN - SCOPUS:0007422443

VL - 8

SP - 588

EP - 598

JO - Chaos

JF - Chaos

SN - 1054-1500

IS - 3

ER -