How neural networks in the human brain represent commonsense knowledge, and
complete related reasoning tasks is an important research topic in
neuroscience, cognitive science, psychology, and artificial intelligence.
Although the traditional artificial neural network using fixed-length vectors
to represent symbols has gained good performance in some specific tasks, it is
still a black box that lacks interpretability, far from how humans perceive the
world. Inspired by the grandmother-cell hypothesis in neuroscience, this work
investigates how population encoding and spiking timing-dependent plasticity
(STDP) mechanisms can be integrated into the learning of spiking neural
networks, and how a population of neurons can represent a symbol via guiding
the completion of sequential firing between different neuron populations. The
neuron populations of different communities together constitute the entire
commonsense knowledge graph, forming a giant graph spiking neural network.
Moreover, we introduced the Reward-modulated spiking timing-dependent
plasticity (R-STDP) mechanism to simulate the biological reinforcement learning
process and completed the related reasoning tasks accordingly, achieving
comparable accuracy and faster convergence speed than the graph convolutional
artificial neural networks. For the fields of neuroscience and cognitive
science, the work in this paper provided the foundation of computational
modeling for further exploration of the way the human brain represents
commonsense knowledge. For the field of artificial intelligence, this paper
indicated the exploration direction for realizing a more robust and
interpretable neural network by constructing a commonsense knowledge
representation and reasoning spiking neural networks with solid biological
plausibility.

本文研究了神经元人口编码和峰值时间相关可塑性机制如何整合进学习脉冲神经网络中，以及如何通过奖励调节峰值时间相关可塑性机制实现相关推理任务，并构造巨型知识图的脉冲神经网络表征。