Complex spiking neural networks with synaptic time-delay based on anti-interference function

Abstract

The research on a brain-like model with bio-interpretability is conductive to promoting its information processing ability in the field of artificial intelligence. Biological results show that the synaptic time-delay can improve the information processing abilities of the nervous system, which are an important factor related to the formation of brain cognitive functions. However, the synaptic plasticity with time-delay of a brain-like model still lacks bio-interpretability. In this study, combining excitatory and inhibitory synapses, we construct the complex spiking neural networks (CSNNs) with synaptic time-delay that more conforms biological characteristics, in which the topology has scale-free property and small-world property, and the nodes are represented by an Izhikevich neuron model. Then, the information processing abilities of CSNNs with different types of synaptic time-delay are comparatively evaluated based on the anti-interference function, and the mechanism of this function is discussed. Using two indicators of the anti-interference function and three kinds of noise, our simulation results consistently verify that: (i) From the perspective of anti-interference function, an CSNN with synaptic random time-delay outperforms an CSNN with synaptic fixed time-delay, which in turn outperforms an CSNN with synaptic none time-delay. The results imply that brain-like networks with more bio-interpretable synaptic time-delay have stronger information processing abilities. (ii) The synaptic plasticity is the intrinsic factor of the anti-interference function of CSNNs with different types of synaptic time-delay. (iii) The synaptic random time-delay makes an CSNN present better topological characteristics, which can improve the information processing ability of a brain-like network. It implies that synaptic time-delay is a factor that affects the anti-interference function at the level of performance.

Keywords: Anti-interference; Complex network; Spiking neural network; Synaptic plasticity; Synaptic time-delay.

Fig. 1

Firing patterns for Izhikevich neurons. a Excitatory firing pattern. b Inhibitory firing pattern

Fig. 2

Comparison of $\delta$ for CSNNs with different $\lambda$ under different interference. a White Gaussian noise. b Impulse noise. c Electric field noise

Fig. 3

Under the same simulation conditions as $\delta$, the averages of five results for $\rho$ of CSNNs with different $\lambda$ under different interference are shown in Fig. 3. Comparison of $\rho$ for CSNNs with different $\lambda$ under different interference. a White Gaussian noise. b Impulse noise. c Electric field noise

Fig. 4

Comparison of $\delta$ for CSNNs with different types of synaptic time-delay. a White Gaussian noise. b Impulse noise. c Electric field noise

Fig. 5

Comparison of $\rho$ for CSNNs with different types of synaptic time-delay. a White Gaussian noise. b Impulse noise. c Electric field noise

Fig. 6

Firing sequences of Izhikevich neurons under interference. a Excitatory firing. b Inhibitory firing

Fig. 7

Evolution of the average firing rate

Fig. 8

The evolution of the average firing rate and its $\delta$. a Average firing rate. b $\delta$

Fig. 9

Evolution of the average synaptic weight

Fig. 10

Evolution of the anti-interference function. a Evolution of $\delta$. b Evolution of $\rho$

Fig. 11

Evolution of topological characteristics. a Average clustering coefficient. b Average shortest path length