Data_Sheet_1_A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support.pdf

Data-driven spiking neuronal network (SNN) models enable in-silico 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 Drosophila 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.

数据驱动型脉冲神经网络（spiking neuronal network, SNN）模型可在细胞与突触层面开展神经系统的计算机模拟分析，因此是解析大脑信息处理原理的核心工具。尽管大量研究聚焦于开发适用于哺乳动物大脑的数据驱动型SNN模型，但其自身的复杂性给精度优化带来了诸多挑战：网络拓扑结构往往依赖统计推断，而特定脑区的功能与配套神经元活动的机制仍未明确；此外，此类模型需要海量计算资源，且模拟速度远慢于真实生物进程。本研究提出一款轻量化数据驱动型SNN模型，在简洁性与可复现性之间达成了平衡。该模型采用定性建模方法构建，可复现神经元活动的关键动力学特征。研究以果蝇嗅觉神经系统为对象，从连接组数据中提取其网络拓扑结构。该模型已成功在小型入门级现场可编程门阵列（field-programmable gate array, FPGA）上部署，并实现了神经网络活动的实时模拟。此外，该模型成功复现了嗅觉系统的核心功能——嗅觉联想学习，以及不同神经元类型的特征性脉冲活动。综上，本文提出了一种从生物数据构建数据驱动型SNN模型的方法。本方法可复现神经系统的功能与神经元活动，且具备轻量化特性，可通过专用硬件实现加速，因此可扩展至大规模神经网络。因此，本方法有望通过“构建式分析”手段，在细胞与突触层面助力解析大脑的信息处理机制。此外，该方法未来有望应用于边缘人工智能系统。