School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, PR China.
School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, PR China.
Neural Netw. 2024 Dec;180:106727. doi: 10.1016/j.neunet.2024.106727. Epub 2024 Sep 10.
Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky-Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.
分布式神经形态架构是一种很有前途的用于多个任务的片上处理技术。然而，由于网络拓扑、连接规则以及与多种编程语言的兼容性等考虑因素，将构建的模型部署到分布式神经形态系统中仍然是一项耗时且具有挑战性的任务。我们提出了一个多尺度分布式神经计算模型数据库（NCMD），这是一个专为基于 ARM 的多核硬件设计的框架。NCMD 包含了各种神经计算组件，包括离子通道、突触和神经元。我们展示了如何在 BrainS 中构建和部署多腔室详细神经元模型和尖峰神经网络（SNN），BrainS 是一个分布式多 ARM 神经形态系统。我们证明了 NCMD 开发的电扩散 Pinsky-Rinzel（edPR）模型非常适合 BrainS。所有动态特性，如膜电位和离子浓度的变化，都可以轻松探索。此外，NCMD 构建的 SNN 在鸢尾花数据集的测试集上可以达到 86.67%的准确率。所提出的 NCMD 为在神经科学、认知决策和人工智能研究中应用 BrainS 提供了一种创新方法。
