# Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces

arXiv: https://arxiv.org/abs/2312.15889 · HTML: https://mlsanity.com/paper/2312.15889/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 71 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Machine learning for neural decoding](https://mlsanity.com/paper/1708.00909/) (2017)
- [Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions](https://mlsanity.com/paper/2203.07006/) (2022)
- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces](https://mlsanity.com/paper/1509.07450/) (2015)
- [An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface](https://mlsanity.com/paper/2210.06287/) (2022)
- [Edge Deep Learning for Neural Implants](https://mlsanity.com/paper/2012.00307/) (2020)
- [Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs](https://mlsanity.com/paper/2204.02362/) (2022)
- [Real-time Closed Loop Neural Decoding on a Neuromorphic Chip](https://mlsanity.com/paper/1812.03991/) (2018)

## Built on by (most cited first, 3 of 3)

- [Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings](https://mlsanity.com/paper/2410.22283/) (2024)
- [Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI](https://mlsanity.com/paper/2505.05983/) (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)

While it is important to make implantable brain-machine interfaces (iBMI) wireless to increase patient comfort and safety, the trend of increased channel count in recent neural probes poses a challenge due to the concomitant increase in the data rate. Extracting information from raw data at the source by using edge computing is a promising solution to this problem, with integrated intention decoders providing the best compression ratio. Recent benchmarking efforts have shown recurrent neural networks to be the best solution. Spiking Neural Networks (SNN) emerge as a promising solution for resource efficient neural decoding while Long Short Term Memory (LSTM) networks achieve the best accuracy. In this work, we show that combining traditional signal processing techniques, namely signal filtering, with SNNs improve their decoding performance significantly for regression tasks, closing the gap with LSTMs, at little added cost. Results with different filters are shown with Bessel filters providing best performance. Two block-bidirectional Bessel filters have been used--one for low latency and another for high accuracy. Adding the high accuracy variant of the Bessel filters to the output of ANN, SNN and variants provided statistically significant benefits with maximum gains of ≈ 5% and 8% in R2 for two SNN topologies (SNNStreaming and SNN3D). Our work presents state of the art results for this dataset and paves the way for decoder-integrated-implants of the future.