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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