Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence
Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence
复制标题
海马网络在数字神经形态系统上的可扩展实现，以实现类脑智能
DOI：
10.3390/app10082857
复制
发表时间：
2020
影响因子：
2.7
通讯作者：
Yang Shuangming
中科院分区：
文献类型：
--
作者：
Sun Wei;Wang Jiang;Zhang Nan;Yang Shuangming
本文创新性地提出了一种扩展的数字海马突波神经网络（HSNN）来模拟哺乳动物的认知系统，并实现在大脑的认知过程中起关键作用的神经调节动力学，如记忆和学习。可扩展的片上网络和并行拓扑结构可以实现大规模峰值神经网络的实时计算。通过探索神经元领域的最新研究成果，并与本文的研究结果进行对比，可以发现，采用坐标旋转数值计算算法实现海马神经元模型，可以显著降低硬件资源的开销。此外，合理使用片上网络技术可以进一步提高系统的性能，甚至可以显著提高单个现场可编程门阵列芯片上的网络可扩展性。在所提出的系统中考虑了神经调制动力学，其可以复制更多相关的生物动力学。基于生物学理论和硬件集成理论的分析，表明本文提出的创新系统能够再现海马网络的生物学特性，并有可能应用于脑启发智能主体。本论文的研究将对未来的棘波神经网络的数字化神经形态设计和海马网络动力学的研究产生意想不到的效果。
In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the brain, such as memory and learning. The real-time computation of a large-scale peak neural network can be realized by the scalable on-chip network and parallel topology. By exploring the latest research in the field of neurons and comparing with the results of this paper, it can be found that the implementation of the hippocampal neuron model using the coordinate rotation numerical calculation algorithm can significantly reduce the cost of hardware resources. In addition, the rational use of on-chip network technology can further improve the performance of the system, and even significantly improve the network scalability on a single field programmable gate array chip. The neuromodulation dynamics are considered in the proposed system, which can replicate more relevant biological dynamics. Based on the analysis of biological theory and the theory of hardware integration, it is shown that the innovative system proposed in this paper can reproduce the biological characteristics of the hippocampal network and may be applied to brain-inspired intelligent subjects. The study in this paper will have an unexpected effect on the future research of digital neuromorphic design of spike neural network and the dynamics of the hippocampal network.