Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.
Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is still unclear how distinct local network states, which are intrinsically generated by synaptic interactions within each layer, would affect the ability of FFN to transmit information. Here we investigate the impact of such network states on propagating transient synchrony (synfire) and firing rate by a combination of numerical simulations and analytical approach. Specifically, local network dynamics is attributed to the competition between excitatory and inhibitory neurons within each layer. Our results show that concomitant with different local network states, the performance of signal propagation differs dramatically. For both synfire propagation and firing rate propagation, there exists an optimal local excitability state, respectively, that optimizes the performance of signal propagation. Furthermore, we find that long-range connections strongly change the dependence of spiking activity propagation on local network state and propose that these two factors work jointly to determine information transmission across distributed networks. Finally, a simple mean field approach that bridges response properties of long-range connectivity and local subnetworks is utilized to reveal the underlying mechanism.
School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People's Republic of China.
Ruixue
R
Han
School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People's Republic of China.
Jiang
J
Wang
School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People's Republic of China.
Haitao
H
Yu
School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People's Republic of China.
Bin
B
Deng
School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People's Republic of China.
Xilei
X
Wei
School of Automation and Electrical Engineering, Tianjin University of Technology and Education Tianjin, Tianjin 300222, China.
Yingmei
Y
Qin
Department of Statistics and Actuarial Science, Simon Fraser University, 507-9188 University Crescent, Burnaby BC V5A 0A5, Canada.
Haixu
H
Wang
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology; D003071:Cognition; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism / Q000473:pathology* / Q000502:physiology; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D009435:Synaptic Transmission / Q000502:physiology