Neuromorphic Simulation of Drosophila Melanogaster Brain Connectome on Loihi 2

TL;DR

This paper presents the first neuromorphic simulation of the entire Drosophila melanogaster brain connectome on the Loihi 2 platform, demonstrating significant speed advantages and addressing hardware constraints for realistic biological neural networks.

Contribution

It introduces a scalable method to simulate a complex biological connectome on neuromorphic hardware, overcoming hardware limitations and validating biological plausibility.

Findings

Full connectome fits on 12 Loihi 2 chips

Simulation is orders of magnitude faster than traditional methods

Performance improves with sparser neural activity

Abstract

We demonstrate the first-ever nontrivial, biologically realistic connectome simulated on neuromorphic computing hardware. Specifically, we implement the whole-brain connectome of the adult Drosophila melanogaster (fruit fly) from the FlyWire Consortium containing 140K neurons and 50M synapses on the Intel Loihi 2 neuromorphic platform. This task is particularly challenging due to the characteristic connectivity structure of biological networks. Unlike artificial neural networks and most abstracted neural models, real biological circuits exhibit sparse, recurrent, and irregular connectivity that is poorly suited to conventional computing methods intended for dense linear algebra. Though neuromorphic hardware is architecturally better suited to discrete event-based biological communication, mapping the connectivity structure to frontier systems still faces challenges from low-level hardware constraints, such as fan-in and fan-out memory limitations. We describe solutions to these challenges that allow for the full FlyWire connectome to fit onto 12 Loihi 2 chips. We statistically validate our implementation by comparing network behavior across multiple reference simulations. Significantly, we achieve a neuromorphic implementation that is orders of magnitude faster than numerical simulations on conventional hardware, and we also find that performance advantages increase with sparser activity. These results affirm that today's scalable neuromorphic platforms are capable of implementing and accelerating biologically realistic models -- a key enabling technology for advancing neuro-inspired AI and computational neuroscience.