Guo Lei, Zhang Sijia, Wu Youxi, Xu Guizhi
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130 China.
Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, 300130 China.
Cogn Neurodyn. 2022 Dec;16(6):1485-1503. doi: 10.1007/s11571-022-09803-4. Epub 2022 Apr 15.
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.
对具有生物可解释性的类脑模型进行研究，有助于提升其在人工智能领域的信息处理能力。生物学研究结果表明，突触时延能够提高神经系统的信息处理能力，这是与大脑认知功能形成相关的一个重要因素。然而，具有时延的类脑模型的突触可塑性仍缺乏生物可解释性。在本研究中，我们结合兴奋性和抑制性突触，构建了更符合生物学特征的具有突触时延的复杂脉冲神经网络（CSNNs），其拓扑结构具有无标度特性和小世界特性，节点由Izhikevich神经元模型表示。然后，基于抗干扰功能对不同类型突触时延的CSNNs的信息处理能力进行了比较评估，并探讨了该功能的机制。利用抗干扰功能的两个指标和三种噪声，我们的仿真结果一致验证了：（i）从抗干扰功能的角度来看，具有突触随机时延的CSNN优于具有突触固定时延的CSNN，而具有突触固定时延的CSNN又优于没有突触时延的CSNN。结果表明，具有更多生物可解释性突触时延的类脑网络具有更强的信息处理能力。（ii）突触可塑性是不同类型突触时延的CSNNs抗干扰功能的内在因素。（iii）突触随机时延使CSNN呈现出更好的拓扑特性，这可以提高类脑网络的信息处理能力。这意味着突触时延是在性能层面影响抗干扰功能的一个因素。
