Nanami Takuya, Yamada Daichi, Someya Makoto, Hige Toshihide, Kazama Hokto, Kohno Takashi
Institute of Industrial Science, The University of Tokyo, Meguro Ku, Tokyo, Japan.
Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Front Neurosci. 2024 Jun 26;18:1384336. doi: 10.3389/fnins.2024.1384336. eCollection 2024.
Data-driven spiking neuronal network (SNN) models enable analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.
数据驱动的脉冲神经网络（SNN）模型能够在细胞和突触水平上分析神经系统。因此，它们是阐明大脑信息处理原理的关键工具。尽管广泛的研究集中在为哺乳动物大脑开发数据驱动的SNN模型，但它们的复杂性在实现精确性方面带来了挑战。网络拓扑通常依赖于统计推断，特定脑区的功能和支持神经元活动仍不清楚。此外，这些模型需要巨大的计算设施，并且它们的模拟速度比实时速度慢得多。在此，我们提出一种轻量级的数据驱动SNN模型，该模型在简单性和可重复性之间取得平衡。该模型使用定性建模方法构建，能够重现神经元活动的关键动态。我们以嗅觉神经系统为目标，从连接组数据中提取其网络拓扑。该模型成功地在小型入门级现场可编程门阵列上实现，并实时模拟了网络活动。此外，该模型重现了嗅觉联想学习，即嗅觉系统的主要功能，以及不同神经元类型的特征性脉冲活动。总之，本文提出了一种从生物学数据构建数据驱动SNN模型的方法。我们的方法重现了神经系统的功能和神经元活动，并且是轻量级的，可通过专用硬件加速，使其能够扩展到大规模网络。因此，我们的方法有望通过构建分析方法在细胞和突触水平上阐明大脑的信息处理中发挥重要作用。此外，它未来可能适用于边缘人工智能系统。
