# An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface

arXiv: https://arxiv.org/abs/2210.06287 · HTML: https://mlsanity.com/paper/2210.06287/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Machine learning for neural decoding](https://mlsanity.com/paper/1708.00909/) (2017)
- [BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation](https://mlsanity.com/paper/1812.09835/) (2018)

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices](https://mlsanity.com/paper/2303.10780/) (2023)
- [Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces](https://mlsanity.com/paper/2312.15889/) (2023)
- [Decoding finger velocity from cortical spike trains with recurrent spiking neural networks](https://mlsanity.com/paper/2409.01762/) (2024)
- [Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces](https://mlsanity.com/paper/2409.04428/) (2024)
- [Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI](https://mlsanity.com/paper/2505.05983/) (2025)
- [Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity](https://mlsanity.com/paper/2506.13400/) (2025)
- [A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks](https://mlsanity.com/paper/2510.20683/) (2025)
- [An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface](https://mlsanity.com/paper/2511.22108/) (2025)

## Abstract (arXiv)

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.