Towards close-to-nature neural networks

Author

Stoop, R. ; Bunimovich, L.A.

Author_Institution

Inst. fur Neuroinf., ETHZ, Switzerland

Volume

1

fYear

1999

fDate

1999

Firstpage

577

Abstract

When regularly spiking rat cortical cells perturbed by periodic inhibition, for a set of positive measures of specific ratios between stimulation and self-oscillation frequency, the resulting spiking pattern is chaotic. Contrary to earlier speculations, these connections do not desynchronize the network. The optimal network performance is characterized by a transition from local chaos to global chaos dominance. When a phase-coincidence detection algorithm is applied, quick convergence towards nontrivial phase patterns is observed. Distinct “sensory” inputs to the network are reflected in localized, input-specific differences of the observed attractors

Keywords

brain models; cellular biophysics; chaos; neural nets; neurophysiology; nonlinear dynamical systems; attractors; chaotic response; close-to-nature neural networks; global chaos; local chaos; localized input-specific differences; nonlinear dynamics; nontrivial phase patterns; optimal network performance; periodic inhibition; periodic inhibitory stimulation; phase-coincidence detection algorithm; quick convergence; rat cortical cells; self-oscillation frequency; sensory inputs; spiking pattern; stimulation; transition; Artificial neural networks; Biological neural networks; Biological system modeling; Brain modeling; Chaos; Displays; Frequency; Glass; In vitro; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on

Conference_Location

Pafos

Print_ISBN

0-7803-5682-9

Type

conf

DOI

10.1109/ICECS.1999.812351

Filename

812351