基于类脑脉冲神经网络的符号表征与推理 | |
方宏坚![]() | |
2022-08-17 | |
页数 | 146 |
学位类型 | 博士 |
中文摘要 | 本文基于当前神经科学领域关于符号表征与推理的研究成果,构建了多环 路协同的类脑脉冲神经网络模型,系统性地针对脉冲神经网络如何完成符号表 征以及推理任务展开讨论与研究。从模型功能和结构上看,本文从微观尺度,介 观尺度和宏观尺度分别对大脑的连接结构和功能进行了借鉴。在微观尺度上,本 文采用了类生物神经元的计算模型,引入脉冲时序依赖可塑性机制 (STDP),以 及奖励信号调制的脉冲时序依赖可塑性机制 (R-STDP) 来建立网络中神经元间的 微观突触结构。在介观尺度上,本文受神经元群体编码机制启发,以神经元组完 成对于概念符号的表征,并通过学习与记忆使得不同神经元组间形成稳定的突 触连接,构建了多环路协同类脑脉冲神经网络实现符号序列表征与重建任务。宏 观功能层面,本文详细阐述了基于数据驱动的深度神经网络缺乏因果推理能力, 与人类的智能存在本质上的差异,并在宏观功能层面探索验证了类脑脉冲神经 网络完成因果推理的可行性。 |
英文摘要 | Based on current research results in neuroscience on symbolic representation and reasoning, this paper constructs a multi-loop collaborative brain-inspired spiking neural network model, and systematically discusses and investigates how spiking neural networks accomplish symbolic representation and reasoning tasks. In terms of model function and structure, this paper draws on the brain's function and connectivity structure at the microscopic, mesoscopic and macroscopic scales respectively. At the microscopic scale, the paper adopts a computational model of biological neuron-like neurons and introduces spiking timing-dependent plasticity mechanisms for synapses as well as reward-modulated spike timing-dependent plasticity mechanisms to build the micro-structure of neural networks. At the mesoscopic scale, inspired by the population coding mechanism, this paper uses neuron populations to complete the representation of symbols, and through learning and memory to form stable synaptic connections between different neuronal populations, a multi-loop synergistic brain-inspired spiking neural network is constructed to achieve the task of symbol sequence representation and reconstruction. At the macro level, the paper elaborates that data-driven deep neural networks lack causal reasoning and are fundamentally different from human intelligence, and explores the feasibility of brain-inspired spiking neural networks for causal reasoning at the macro functional level.
First, Brain-inspired Sequence Production Spiking Neural Network (SP-SNN). Inspired by the related research on how macaques complete symbolic representation and symbolic sequence production in neuroscience, this paper proposes a multi-loop coordinated brain-inspired symbolic sequence production spiking neural network to simulate the same symbolic sequence memory and production process as in macaques. After experimental verification, it is proved that SP-SNN can complete the working memory of the symbol sequence by fusing the population coding representation mechanism and the spiking timing-dependent plasticity mechanism (STDP). Moreover, by introducing the reward-modulated spike timing-dependent plasticity (R-STDP) mechanism based on the modulation of the reward signal, after reinforcement learning, SP-SNN can produce the symbol sequence according to different supra-regular grammar rules. Moreover, SP-SNN's performance in the experiment can be well fitted with the behavioral data of macaques, which further demonstrates the biological plausibility of the network. In addition, this work also verifies that the chunking mechanism plays an essential role in improving the robustness of the model. Second, Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning (KRR-GSNN). Inspired by the neural mechanism of the neuron population coding symbolic concept in the biological brain, KRR-GSNN combines the population coding representation mechanism and the spike-timing-dependent plasticity (STDP) learning rule to encode the commonsense knowledge in the commonsense knowledge graph into a large-scale spike neural network and completed subsequent related reasoning tasks accordingly. Each neuron population jointly encodes the entire commonsense knowledge graph through synaptic connections, forming a graph spiking neural network that records commonsense knowledge. Furthermore, the reward-modulated spike-timing-dependent plasticity mechanism (R-STDP) is introduced to realize the biological-inspired self-supervised reinforcement learning process and complete related reasoning tasks. KRR-GSNN achieves comparable accuracy and faster convergence for the same reasoning task than graph convolutional artificial neural networks. Third, Brain-Inspired Causal Reasoning Spiking Neural Networks (CR-SNN). The current data-driven deep neural network methods essentially only utilize the correlation between data, difficult to see the causal nature of the world through data. Empowering machines with causal reasoning ability is a critical step toward a higher level of general intelligence. Inspired by the reasoning method based on the causal graph, we propose brain-inspired causal reasoning spiking neural network model by combining the relevant neural mechanisms and connection structures to solve this problem. CR-SNN completes the construction of a static causal graph by using the population coding mechanism and the learning rule of spiking timing-dependent plasticity. The feasibility of causal reasoning by spiking neural networks (SNN) is proved through experimental verification. Moreover, by introducing an external evaluation function, observers can know the reasoning path of the network in the completion of the reasoning process according to the firing state of the network, thereby significantly improving the interpretability of the network, laying a foundation for further exploration and construction of human-comprehensible spiking neural network systems. |
关键词 | 类脑人工智能 脉冲神经网络 符号表征 突触可塑性 因果推理 |
语种 | 中文 |
七大方向——子方向分类 | 类脑模型与计算 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
文献类型 | 学位论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49912 |
专题 | 毕业生_博士学位论文 |
通讯作者 | 方宏坚 |
推荐引用方式 GB/T 7714 | 方宏坚. 基于类脑脉冲神经网络的符号表征与推理[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022. |
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