A lightweight data-driven spiking neural network model of Drosophila olfactory nervous system with dedicated hardware support

Abstract

Data-driven spiking neural network (SNN) models are vital for understanding the brains information processing at the cellular and synaptic level. 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 significant computational resources. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model implemented on an entry-level field-programmable gate array successfully reproduced the functions and characteristic spiking activities of different neuron types. Our approach thus provides a foundation for constructing lightweight in silico models that are critical for investigating the brains information processing mechanisms at the cellular and synaptic level through an analysis-by-construction approach and applicable to edge artificial intelligence (AI) systems.