Event-based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems
Year: 2025
Type: conference-paper
Abstract: Implantable brain-machine interfaces (iBMIs) are evolving to record from thousands of neurons wirelessly but face challenges in data bandwidth, power consumption, and implant size. We propose a novel Spiking Neural Network Spike Detector (SNN-SPD) that processes event-based neural data generated via delta modulation and pulse count modulation, converting signals into sparse events. By leveraging the temporal dynamics and inherent sparsity of spiking neural networks, our method improves spike detection performance while maintaining low computational overhead suitable for implantable devices. Our experimental results demonstrate that the proposed SNN-SPD achieves an accuracy of 95.72% at high noise levels (standard deviation 0.2), which is about 2% higher than the existing Artificial Neural Network Spike Detector (ANN-SPD). Moreover, SNN-SPD requires only 0.41% of the computation and about 26.62% of the weight parameters compared to ANN-SPD, with zero multiplications. This approach balances efficiency and performance, enabling effective data compression and power savings for next-generation iBMIs.
Authors: Chanwook Hwang, Biyan Zhou, Ye Ke, Vivek Mohan, Jong Hwan Ko, Arindam Basu
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Research Foundation
OpenAlex ID: W4411725149