An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface

• URL: http://arxiv.org/abs/2511.22108v1
• Date: Thu, 27 Nov 2025 04:56:17 GMT
• Title: An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface
• Authors: Zhou Biyan, Arindam Basu,
• Abstract summary: Deep Spiking Neural Networks (DSNNs) are being recognized as a promising approach for developing resource-efficient neural decoders.<n>We propose continuous learning approaches with Reinforcement Learning (RL) algorithms adapted for DSNNs.<n>The accuracy of open-loop experiments conducted with DSNN Banditron and DSNN AGREL remains stable over extended periods.
• Score: 1.8363218208079994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The number of simultaneously recorded neurons follows an exponentially increasing trend in implantable brain-machine interfaces (iBMIs). Integrating the neural decoder in the implant is an effective data compression method for future wireless iBMIs. However, the non-stationarity of the system makes the performance of the decoder unreliable. To avoid frequent retraining of the decoder and to ensure the safety and comfort of the iBMI user, continuous learning is essential for real-life applications. Since Deep Spiking Neural Networks (DSNNs) are being recognized as a promising approach for developing resource-efficient neural decoder, we propose continuous learning approaches with Reinforcement Learning (RL) algorithms adapted for DSNNs. Banditron and AGREL are chosen as the two candidate RL algorithms since they can be trained with limited computational resources, effectively addressing the non-stationary problem and fitting the energy constraints of implantable devices. To assess the effectiveness of the proposed methods, we conducted both open-loop and closed-loop experiments. The accuracy of open-loop experiments conducted with DSNN Banditron and DSNN AGREL remains stable over extended periods. Meanwhile, the time-to-target in the closed-loop experiment with perturbations, DSNN Banditron performed comparably to that of DSNN AGREL while achieving reductions of 98% in memory access usage and 99% in the requirements for multiply- and-accumulate (MAC) operations during training. Compared to previous continuous learning SNN decoders, DSNN Banditron requires 98% less computes making it a prime candidate for future wireless iBMI systems.

Related papers

• SteganoSNN: SNN-Based Audio-in-Image Steganography with Encryption [1.3483884526104932]
  This work introduces SteganoSNN, a neuromorphic steganographic framework that exploits spiking neural networks (SNNs) to achieve secure, low-power, and high-capacity multimedia data hiding.<n> Digitised audio samples are converted into spike trains using leaky integrate-and-fire neurons, encrypted via a modulo-based mapping scheme, and embedded into the least significant bits of RGBA image channels.
  arXiv  Detail & Related papers  (2025-11-09T23:31:53Z)
• Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method [0.0]
  Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation.<n>This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm.
  arXiv  Detail & Related papers  (2025-07-28T17:09:48Z)
• On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks [1.0533738606966752]
  This paper studies the use of Spiking Neural Networks (SNNs) for performing direct robot navigation and obstacle avoidance from LIDAR data.<n>By carefully tuning the membrane potential leakage constant of the spiking Leaky Integrate-and-Fire neurons used within our SNN, it is possible to achieve on-par robot control precision.<n>The LIDAR dataset collected during this work is released as open-source with the hope of benefiting future research.
  arXiv  Detail & Related papers  (2025-07-13T08:43:31Z)
• Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control [59.65431931190187]
  Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making on neuromorphic hardware.<n>Most continuous control algorithms for continuous control are designed for Artificial Neural Networks (ANNs)<n>We show that this mismatch destabilizes SNN training and degrades performance.<n>We propose a novel proxy target framework to bridge the gap between discrete SNNs and continuous-control algorithms.
  arXiv  Detail & Related papers  (2025-05-30T03:08:03Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface [11.786044345820459]
  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.
  arXiv  Detail & Related papers  (2022-10-07T12:58:28Z)
• DNN Training Acceleration via Exploring GPGPU Friendly Sparsity [16.406482603838157]
  We propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and online generated row-based or tile-based dropout patterns. We then develop a SGD-based Search Algorithm that produces the distribution of row-based or tile-based dropout patterns to compensate for the potential accuracy loss. We also propose the sensitivity-aware dropout method to dynamically drop the input feature maps based on their sensitivity so as to achieve greater forward and backward training acceleration.
  arXiv  Detail & Related papers  (2022-03-11T01:32:03Z)
• Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning [60.20150317299749]
  This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning. To make full use of the training data, we propose a full data learning method for speech enhancement.
  arXiv  Detail & Related papers  (2020-11-11T06:32:37Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.