Event-based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems

TL;DR

This paper introduces a spiking neural network-based spike detector for implantable brain-machine interfaces, achieving high accuracy with significantly reduced computational resources and power consumption, suitable for wireless neural data processing.

Contribution

The paper presents a novel SNN-based spike detection method that enhances performance and efficiency over traditional neural networks for iBMI applications.

Findings

Achieves 95.72% accuracy at high noise levels.

Uses only 0.41% of the computation of traditional methods.

Requires about 26.62% of the weight parameters.

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.