Coherence resonance in bursting neural networks

June Hoan Kim, Ho Jun Lee, Cheol Hong Min, Kyoung Jin Lee

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal - a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.

Original languageEnglish
Article number042701
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume92
Issue number4
DOIs
Publication statusPublished - 2015 Oct 1

Fingerprint

Coherence Resonance
Bursting
Neural Networks
Burst
Regularity
regularity
bursts
Neuroscience
Complex Dynamics
neurology
Neural Network Model
Randomness
Attractor
Manipulation
Computer Simulation
manipulators
Interval
computerized simulation
intervals
Demonstrate

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability

Cite this

Coherence resonance in bursting neural networks. / Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung Jin.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 92, No. 4, 042701, 01.10.2015.

Research output: Contribution to journalArticle

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