Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity
This work addresses the need for real-time, low-power neural decoding to improve wireless brain-machine interfaces for paralyzed patients, though it appears incremental as it builds on prior challenges and methods.
The authors tackled the problem of low-power neural decoding for wireless intra-cortical brain-machine interfaces by optimizing a spiking neural network architecture, achieving state-of-the-art results on the Primate Reaching dataset while maintaining similar resource demands.
Intra-cortical brain-machine interfaces (iBMIs) present a promising solution to restoring and decoding brain activity lost due to injury. However, patients with such neuroprosthetics suffer from permanent skull openings resulting from the devices' bulky wiring. This drives the development of wireless iBMIs, which demand low power consumption and small device footprint. Most recently, spiking neural networks (SNNs) have been researched as potential candidates for low-power neural decoding. In this work, we present the next step of utilizing SNNs for such tasks, building on the recently published results of the 2024 Grand Challenge on Neural Decoding Challenge for Motor Control of non-Human Primates. We optimize our model architecture to exceed the existing state of the art on the Primate Reaching dataset while maintaining similar resource demand through various compression techniques. We further focus on implementing a realtime-capable version of the model and discuss the implications of this architecture. With this, we advance one step towards latency-free decoding of cortical spike trains using neuromorphic technology, ultimately improving the lives of millions of paralyzed patients.