Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Coherence resonance in bursting neural networks

Authors
Kim, June HoanLee, Ho JunMin, Cheol HongLee, Kyoung J.
Issue Date
1-Oct-2015
Publisher
AMER PHYSICAL SOC
Citation
PHYSICAL REVIEW E, v.92, no.4
Indexed
SCIE
SCOPUS
Journal Title
PHYSICAL REVIEW E
Volume
92
Number
4
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/92224
DOI
10.1103/PhysRevE.92.042701
ISSN
1539-3755
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Science > Department of Physics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher LEE, Kyoung Jin photo

LEE, Kyoung Jin
College of Science (Department of Physics)
Read more

Altmetrics

Total Views & Downloads

BROWSE