Title :
Correlated neural activity in spiking networks with topographic couplings
Author :
Jinli Xie;Qinjun Zhao;Jianyu Zhao
Author_Institution :
School of Electrical Engineering, University of Jinan, Jinan, 250022, China
Abstract :
A neural field model with topographic feedforward and feedback is chosen to confirm how correlated activity in spiking neural networks depends on spatial couplings. Numerical simulations reveal that the intensity of the correlated firing is suppressed as the feedback spatial scale decreases. Afterwards, as the feedback becomes topographic, the correlation coefficient is almost unchanged with further decreases in feedback spatial scale. Correspondingly, the relatively flat values of correlation coefficient with decreasing feedforward spatial scale imply that the effect of spatial spread of feedforward on correlated firing of the neurons keeps almost invariable. Therefore, the correlation coefficient and the spatial scales are uncorrelated in the network with topographic couplings. In brief, the global feedback enables the system to modulate correlated neural activity with the spatial scale, while the introduction of topography in couplings bring little effect on network correlations.
Keywords :
"Feedforward neural networks","Neurons","Firing","Couplings","Correlation","Correlation coefficient","Encoding"
Conference_Titel :
Advanced Mechatronic Systems (ICAMechS), 2015 International Conference on
Electronic_ISBN :
2325-0690
DOI :
10.1109/ICAMechS.2015.7287132