Real-Time Large-Scale Neural Connectivity Inference on Spiking Neuromorphic System

Abstract

A deep understanding of neuronal circuitry connectivity is essential to replicate biological functions effectively. Inferring neural connectivity considers the cross-correlation of spike timing. Neuromorphic systems utilize online learning algorithms that leverage the temporal correlations of spikes by utilizing spiking neural networks. This research demonstrates real-time, large-scale neural connectivity inference by implementing the presynaptic spike-driven spike-timing-dependent plasticity method on neuromorphic hardware. We validate the capability of the proposed method to perform advanced neuron connectivity inference using synthetic data generated from leaky integrate-and-fire neurons on a multi-scale. Additionally, we analyzed that the proposed method exhibits invariant high inference performance in sparse networks without burst firing, regardless of transmission delay. Finally, we demonstrate the feasibility of the proposed method in real-time neural connectivity inference in actual in vitro or in vivo contexts by conducting neural connectivity inference simulating in a way closely mirroring in vitro conditions through fluorescence imaging signal data.