An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding
for Implantable Brain-Machine Interface
- URL: http://arxiv.org/abs/2210.06287v1
- Date: Fri, 7 Oct 2022 12:58:28 GMT
- Title: An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding
for Implantable Brain-Machine Interface
- Authors: Jiawei Liao, Lars Widmer, Xiaying Wang, Alfio Di Mauro, Samuel R.
Nason-Tomaszewski, Cynthia A. Chestek, Luca Benini, Taekwang Jang
- Abstract summary: We propose a novel neural-power network (SNN) decoder for implantable regression tasks.
The proposed SNN decoder achieves the same level of coefficient correlation as the state-of-the-art ANN decoder in offline finger velocity decoding tasks.
- Score: 11.786044345820459
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Brain-machine interfaces (BMIs) are promising for motor rehabilitation and
mobility augmentation. High-accuracy and low-power algorithms are required to
achieve implantable BMI systems. In this paper, we propose a novel spiking
neural network (SNN) decoder for implantable BMI regression tasks. The SNN is
trained with enhanced spatio-temporal backpropagation to fully leverage its
ability in handling temporal problems. The proposed SNN decoder achieves the
same level of correlation coefficient as the state-of-the-art ANN decoder in
offline finger velocity decoding tasks, while it requires only 6.8% of the
computation operations and 9.4% of the memory access.
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