An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface

TL;DR

This paper introduces an energy-efficient, continuously learning spiking neural network decoder for brain-machine interfaces, significantly reducing computational needs while maintaining stable performance in non-stationary environments.

Contribution

It develops a novel continuous learning approach for DSNNs using RL algorithms, achieving high efficiency and stability suitable for implantable iBMI devices.

Findings

DSNN Banditron reduces memory access by 98%

DSNN Banditron requires 98% less compute than previous methods

Stable accuracy over extended periods in open-loop tests

Abstract

The number of simultaneously recorded neurons follows an exponentially increasing trend in implantable brain-machine interfaces (iBMIs). Integrating the neural decoder in the implant is an effective data compression method for future wireless iBMIs. However, the non-stationarity of the system makes the performance of the decoder unreliable. To avoid frequent retraining of the decoder and to ensure the safety and comfort of the iBMI user, continuous learning is essential for real-life applications. Since Deep Spiking Neural Networks (DSNNs) are being recognized as a promising approach for developing resource-efficient neural decoder, we propose continuous learning approaches with Reinforcement Learning (RL) algorithms adapted for DSNNs. Banditron and AGREL are chosen as the two candidate RL algorithms since they can be trained with limited computational resources, effectively addressing the non-stationary problem and fitting the energy constraints of implantable devices. To assess the effectiveness of the proposed methods, we conducted both open-loop and closed-loop experiments. The accuracy of open-loop experiments conducted with DSNN Banditron and DSNN AGREL remains stable over extended periods. Meanwhile, the time-to-target in the closed-loop experiment with perturbations, DSNN Banditron performed comparably to that of DSNN AGREL while achieving reductions of 98% in memory access usage and 99% in the requirements for multiply- and-accumulate (MAC) operations during training. Compared to previous continuous learning SNN decoders, DSNN Banditron requires 98% less computes making it a prime candidate for future wireless iBMI systems.