Effects of noise and synaptic weight on propagation of subthreshold excitatory postsynaptic current signal in a feed-forward neural network
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Excitatory postsynaptic current (EPSC) is a biological signal of neurons; the propagation mechanism of subthreshold EPSC signal in neural network and the effects of background noise on the propagation of the subthreshold EPSC signal are still unclear. In this paper, considering a feed-forward neural network with five layers and an external subthreshold EPSC signal imposed on the Hodgkin–Huxley neurons of first layer, the propagation and fidelity of subthreshold EPSC signal in the feed-forward neural network are studied by using the spike timing precision and power norm. It is found that the background noise in each layer is beneficial for the propagation of subthreshold EPSC signal in feed-forward neural network; there exists an optimal background noise intensity at which the propagation speed of subthreshold EPSC signal can be enhanced, and the fidelity between system’s response and subthreshold EPSC signal is preserved. The transmission of subthreshold EPSC signal is shifted from failed propagation to succeed propagation with the increasing of synaptic weight. By regulating the background noise and the synaptic weight, the information of subthreshold EPSC signal is transferred accurately through the feed-forward neural network, both time lag and fidelity between the system’s response and subthreshold EPSC signal are promoted. These results might provide a possible underlying mechanism for enhancing the subthreshold EPSC signal propagation.
KeywordsExcitatory postsynaptic current Background noise Synaptic weight Feed-forward neural network
This work was supported by the National Natural Science Foundation of China under Grant Nos.11775091, 11474117, and 11605014.
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Conflict of interest
The authors declare that they have no potential conflict of interest.
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