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Phase constancy in a ladder model of neural dynamics

Clara-Mihaela Ionescu UGent (2012) IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS. 42(6). p.1543-1551
abstract
This paper presents a novel concept of modeling biological systems by means of preserving the natural rules governing the system's dynamics, i.e., their intrinsic fractal (recurrent) structure. The purpose of this paper is to illustrate the capability of recurrent ladder networks to capture the intrinsic recurrent anatomy of neural networks and to provide a dynamic model which shows typical neuronal phenomena, such as the phase constancy. As an illustrating example, the simplified model for a neural network consisting of motor neurons is used in simulation of a recurrent ladder network. Starting from a generalized approach, it is shown that, in the steady state, the result converges to a constant-phase behavior. The outcome of this paper indicates that the proposedmodel is a suitable tool for specific neural models in various neuroscience applications, being able to capture their fractal structure and the corresponding fractal dynamic behavior. A link to the dynamics of EEG activity is suggested. By studying specific neural populations by means of the ladder network model presented in this paper, one might be able to understand the changes observed in the EEG with normal aging or with neurodegenerative disorders.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
fractal structure, Dendritic arbors, fractional calculus, frequency response, ladder network, neural networks, neuron model, phase constancy, recurrent anatomy, self-organized critically, NETWORKS, NEURONS, ADAPTATION, GEOMETRY, SYSTEMS, RAT
journal title
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
IEEE Trans. Syst. Man Cybern. Paart A-Syst. Hum.
volume
42
issue
6
pages
1543 - 1551
Web of Science type
Article
Web of Science id
000310147000020
JCR category
COMPUTER SCIENCE, THEORY & METHODS
JCR impact factor
2.183 (2012)
JCR rank
8/100 (2012)
JCR quartile
1 (2012)
ISSN
1083-4427
DOI
10.1109/TSMCA.2012.2199483
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3051261
handle
http://hdl.handle.net/1854/LU-3051261
date created
2012-11-12 15:36:15
date last changed
2014-04-17 14:08:53
@article{3051261,
  abstract     = {This paper presents a novel concept of modeling biological systems by means of preserving the natural rules governing the system's dynamics, i.e., their intrinsic fractal (recurrent) structure. The purpose of this paper is to illustrate the capability of recurrent ladder networks to capture the intrinsic recurrent anatomy of neural networks and to provide a dynamic model which shows typical neuronal phenomena, such as the phase constancy. As an illustrating example, the simplified model for a neural network consisting of motor neurons is used in simulation of a recurrent ladder network. Starting from a generalized approach, it is shown that, in the steady state, the result converges to a constant-phase behavior. The outcome of this paper indicates that the proposedmodel is a suitable tool for specific neural models in various neuroscience applications, being able to capture their fractal structure and the corresponding fractal dynamic behavior. A link to the dynamics of EEG activity is suggested. By studying specific neural populations by means of the ladder network model presented in this paper, one might be able to understand the changes observed in the EEG with normal aging or with neurodegenerative disorders.},
  author       = {Ionescu, Clara-Mihaela},
  issn         = {1083-4427},
  journal      = {IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS},
  keyword      = {fractal structure,Dendritic arbors,fractional calculus,frequency response,ladder network,neural networks,neuron model,phase constancy,recurrent anatomy,self-organized critically,NETWORKS,NEURONS,ADAPTATION,GEOMETRY,SYSTEMS,RAT},
  language     = {eng},
  number       = {6},
  pages        = {1543--1551},
  title        = {Phase constancy in a ladder model of neural dynamics},
  url          = {http://dx.doi.org/10.1109/TSMCA.2012.2199483},
  volume       = {42},
  year         = {2012},
}

Chicago
Ionescu, Clara-Mihaela. 2012. “Phase Constancy in a Ladder Model of Neural Dynamics.” Ieee Transactions on Systems Man and Cybernetics Part A-systems and Humans 42 (6): 1543–1551.
APA
Ionescu, C.-M. (2012). Phase constancy in a ladder model of neural dynamics. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 42(6), 1543–1551.
Vancouver
1.
Ionescu C-M. Phase constancy in a ladder model of neural dynamics. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS. 2012;42(6):1543–51.
MLA
Ionescu, Clara-Mihaela. “Phase Constancy in a Ladder Model of Neural Dynamics.” IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 42.6 (2012): 1543–1551. Print.