Dygenn dynamic Gaussian weighted evolving Spiking Neural Network model for enhanced neurological disease detection

AstroSNN: astrocyte-augmented spiking neural networks for unsupervised image classification

Simulation results show that incorporating astrocytic modulation consistently enhances classification performance in leaky integrate-and-fire (LIF) networks, including under noisy conditions, and suggest that augmenting simplified astrocytic dynamics can improve robustness and computational capability in SNNs, while also increasing their biological plausibility.

Cognitive Pruning based Spiking Neural Network for Brain Tumor Classification using MRI Images

Spiking Neural Networks for the Analysis of Physiological Signals

Physiological signals, such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG) play a critical role in modern clinical diagnosis, monitoring, and rehabilitation. While deep artificial neural networks (ANNs) have achieved strong performance in analyzing these signals, their high computational cost and energy demands limit deployment in real-time, wearable, and edge-based healthcare systems. Spiking Neural Networks (SNNs), inspired by biological neural computation, offer an event-driven alternative that naturally captures temporal dynamics and enables energy-efficient inference. This article provides a comprehensive review of SNN-based methods for physiological signal analysis across EEG, ECG, and EMG modalities. We survey neuron models, network architectures, encoding schemes, and training methodologies, and systematically review recent state-of-the-art applications in medical diagnosis and rehabilitation. Through analysis, we identify recurring architectural and methodological trends that indicate the dominance of convolution-based spiking architectures, the critical role of encoding strategies in determining energy efficiency, and the task-dependent nature of SNN training approaches. Despite demonstrating performance comparable to conventional deep learning models, often at significantly reduced computational cost, SNN research remains challenged by inconsistent evaluation protocols, limited benchmark standardization, and restricted clinical validation. We conclude by outlining key open challenges and future research directions, emphasizing the need for standardized benchmarks, encoding-aware training, and hybrid ANN–SNN systems. As neuromorphic hardware and event-driven learning methods continue to mature, SNNs are well-positioned to introduce scalable, low-power physiological signal processing for next-generation intelligent healthcare systems.

Burst Spiking Neural Networks

This work proposes Burst Spiking Neural Networks (BuSNNs), built upon Burst-enhanced Spiking Neurons and a Dynamic Weight Constraint (DWC) mechanism, which mitigates perturbation-induced transitions in activation states and thereby enhances robustness.

Noisy group neurons with synchronous resetting for high-performance spiking neural networks

This work proposes a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms, and develops the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics.

SAD-SNN: Spatial-Activation Distillation for High-Performance Spiking Neural Networks

A Spiking Neural Network (SNN) is a kind of brain-inspired and event-driven network, which is becoming a promising energy-efficient alternative to Artificial Neural Networks (ANNs). In recent years, SNN methods have been successfully applied in the fields of electromagnetic signal processing and image signal processing, particularly in application scenarios that require low energy consumption. However, the performance of SNNs by direct training is far from satisfactory. In this paper, we study a novel learning method named SAD-SNN (Spatial-Activation Distillation for Spiking Neural Networks), which utilizes the ANN model to guide the SNN model learning. Unlike prior works that rely on element-wise feature alignment approaches, SAD-SNN aligns spatial-activation maps at different resolutions of the teacher and student networks. Specifically, we introduce a direct alignment approach, which defines a spatial-activation loss and normalizes the representation vectors of ANN and SNN, to alleviate the unexpected precision loss. This enables the knowledge of teacher ANNs to be effectively transferred to train student SNNs. On three image classification datasets, our proposed SAD-SNN outperforms other SNN training methods no matter whether homogeneous or heterogeneous teacher ANNs are used. Furthermore, we apply SAD-SNN to the electromagnetic signal detection task, demonstrating strong generalization ability and superior performance. In conclusion, the experimental results on various tasks and SNN architectures demonstrate that our method is a general and effective solution that significantly improves the learning of student SNNs with only two time steps.