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

## TL;DR

This paper presents a lightweight spiking neuronal network model of the fruit fly's olfactory system that can run in real-time on simple hardware.

## Contribution

A novel lightweight and hardware-accelerated data-driven spiking neuronal network model for the Drosophila olfactory system is introduced.

## Key findings

- The model was implemented on an entry-level FPGA and achieved real-time simulation.
- It successfully reproduced olfactory associative learning and neuron-specific spiking activities.
- The approach is scalable and suitable for analysis-by-construction studies of brain function.

## Abstract

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.