Self-Regulated Complexity in Cultured Neuronal Networks

14 May 2004

journal article
research article
Published by American Physical Society (APS) in Physical Review Letters

Vol. 92 (19) , 198105
https://doi.org/10.1103/physrevlett.92.198105

Abstract

New quantified observables of complexity are identified and utilized to study sequences (time series) recorded during the spontaneous activity of different size cultured networks. The sequence is mapped into a tiled time-frequency domain that maximizes the information about local time-frequency resolutions. The sequence regularity is associated with the domain homogeneity and its complexity with its local and global variations. Shuffling the recorded sequence lowers its complexity down to artificially constructed ones. The new observables are utilized to identify self-regulation motifs in observed complex network activity.

Keywords

This publication has 12 references indexed in Scilit:

Bacterial self–organization: co–enhancement of complexification and adaptability in a dynamic environment
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2003
Formation of Electrically Active Clusterized Neural Networks
Physical Review Letters, 2003
Complexity: The bigger picture
Nature, 2002
Long Term Behavior of Lithographically PreparedIn VitroNeuronal Networks
Physical Review Letters, 2002
The artistry of nature
Nature, 2001
Measures of complexity in neural spike-trains of the slowly adapting stretch receptor organs
Biosystems, 2000
Neuronal–glial interactions and behaviour
Published by Elsevier ,2000
Statistical physics and physiology: Monofractal and multifractal approaches
Physica A: Statistical Mechanics and its Applications, 1999
A Fast Algorithm for Adapted Time–Frequency Tilings
Applied and Computational Harmonic Analysis, 1996
Complexity and Adaptation
Physica D: Nonlinear Phenomena, 1986