mlsanityData to 7 Oct 2026

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

arXiv 2210.06287 ↗First posted 7 Oct 2022eess.SP cs.HC cs.LGcited by 8

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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.

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

Builds on

8 of 23 references.

References · 8

  1. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  3. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  4. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  5. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  6. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 113
  7. 2017Machine learning for neural decodingcited by 45
  8. 2018BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulationcited by 2

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Citing papers per year: 2023: 2; 2024: 2; 2025: 4.

8 papers.

  1. 2023A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practicescited by 8
  2. 2023Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfacescited by 3
  3. 2024Decoding finger velocity from cortical spike trains with recurrent spiking neural networkscited by 1
  4. 2024Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfacescited by 1
  5. 2025Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMIcited by 0
  6. 2025Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activitycited by 0
  7. 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networkscited by 0
  8. 2025An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interfacecited by 0