TY - JOUR
T1 - Effects of degree distributions on signal propagation in noisy feedforward neural networks
AU - Qin, Ying Mei
AU - Che, Yan Qiu
AU - Zhao, Jia
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 61401312 , 31571111 , and 61431013 ), the Natural Science Foundation of Tianjin, China (No. 15JCYBJC19000 and 17JCQNJC03700 ), the Fundamental Research Funds for the Central Universities, China (No. SWU1709620 ) and the support of Tianjin University of Technology and Education, China (No. KYQD14006 ).
Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 61401312, 31571111, and 61431013), the Natural Science Foundation of Tianjin, China (No. 15JCYBJC19000 and 17JCQNJC03700), the Fundamental Research Funds for the Central Universities, China (No. SWU1709620) and the support of Tianjin University of Technology and Education, China (No. KYQD14006).
PY - 2018/12/15
Y1 - 2018/12/15
N2 - We focus on the effects of degree distributions on signal propagation in noisy feedforward networks (FFNs) based on the FitzHugh–Nagumo neuron model. Three FFN topologies are constructed with the same number of synaptic connections in each layer, but different distributions for both the in-degree and out-degree of neurons as identical, uniform and exponential. It is found that the propagation of firing patterns and firing rates are affected by the degree distributions of neurons in the FFNs. The output firing rates in three FFN topologies without noise is nonlinearly dependent on their input firing rates, and it can be increased steadily by increasing noise intensity. The firing patterns of three FFN topologies can also be influenced by the noise and connection probability. Interestingly, an optimal parameter area corresponding to both the noise intensity and connection probability is found for the propagation of spiking regularity in three FFN topologies respectively. In addition, the firing synchronization of different layers in three topologies differs obviously from one another. Moreover, synfire-enhanced coherence resonance emerges in the later layers of the three FFN topologies. These results suggest that the degree distributions of neurons are a key factor that can modulate both the propagation of the firing rates and firing patterns in FFNs.
AB - We focus on the effects of degree distributions on signal propagation in noisy feedforward networks (FFNs) based on the FitzHugh–Nagumo neuron model. Three FFN topologies are constructed with the same number of synaptic connections in each layer, but different distributions for both the in-degree and out-degree of neurons as identical, uniform and exponential. It is found that the propagation of firing patterns and firing rates are affected by the degree distributions of neurons in the FFNs. The output firing rates in three FFN topologies without noise is nonlinearly dependent on their input firing rates, and it can be increased steadily by increasing noise intensity. The firing patterns of three FFN topologies can also be influenced by the noise and connection probability. Interestingly, an optimal parameter area corresponding to both the noise intensity and connection probability is found for the propagation of spiking regularity in three FFN topologies respectively. In addition, the firing synchronization of different layers in three topologies differs obviously from one another. Moreover, synfire-enhanced coherence resonance emerges in the later layers of the three FFN topologies. These results suggest that the degree distributions of neurons are a key factor that can modulate both the propagation of the firing rates and firing patterns in FFNs.
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U2 - 10.1016/j.physa.2018.08.061
DO - 10.1016/j.physa.2018.08.061
M3 - Article
AN - SCOPUS:85051632067
SN - 0378-4371
VL - 512
SP - 763
EP - 774
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
ER -