Title
Stochastic model and neural coding of large-scale neuronal population with variable coupling strength
Abstract
Taking into account the variability of coupling strength with increasing time, we present the nonlinear stochastic dynamical model of neuronal population, where the average number density is introduced as a distributed coding pattern of neuronal population. In the absence of external stimulus, numerical simulations indicate that the synchronized activity of neuronal population increases the coupling strength among neuronal oscillators; the coding pattern of the average number density is related to coupling configuration among neural oscillators. These studies also show that the variability of the coupling strength displays a slow learning process in the weak noise, but the coupling strength exhibits transient process in the strong noise. Numerical simulations confirm that the higher the coupling level is, the larger the synchronization of neuronal population is, and the stronger the coupling strength is.
Year
DOI
Venue
2006
10.1016/j.neucom.2005.05.010
Neurocomputing
Keywords
Field
DocType
coupling level,coupling strength,neural coding,strong noise,variable coupling strength,neuronal population,slow learning process,average number density,neuronal oscillator,large-scale neuronal population,numerical simulation,stochastic model,coding pattern,coupling configuration,oscillations,neural code
Population,Synchronization,Oscillation,Coupling,Nonlinear system,Neural coding,Number density,Stochastic modelling,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
69
7-9
Neurocomputing
Citations 
PageRank 
References 
14
1.55
1
Authors
2
Name
Order
Citations
PageRank
Rubin Wang114125.54
Xianfa Jiao2314.96