Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 49 references
Medicine
Abstract
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.
Spiking neural network (SNN) has drawn substantial research focus due to its high biological interpretability, low energy consumption and effectiveness in time-dependent data processing. However, existing methods fail to fully integrate the biological interpretability of SNN with the physical interpretability of time-frequency transform methods in machinery fault diagnosis. To this end, we propose a spiking time-frequency patching (STFP) spiking neural network model, in which a STFP module is designed for time-frequency feature extraction inside the network, and a spiking temporal–spatial attention module is designed to focus on important features of temporal–spatial dimensions. In addition, the spiking residual network used in the model is also improved in neurons. Extensive experimental results on two datasets of rotating machinery parts demonstrate that, the proposed model achieves superior diagnostic performance benefiting from the designed modules, furnishing an end-to-end fault diagnosis method excelling in accuracy, stability, and interpretability.
Shilong Zhu, Jun Wang, Weiguo Huang et al.· IEEE Transactions on Reliabi...· 0 citations
The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce runtime multiplications, which weaken the hardware-efficiency motivation of SNNs. To address this tension, we identify catastrophic firing-rate decay as a primary cause of severe performance degradation in normalization-free SNNs. Guided by this insight, this work proposes the Intrinsically Stable SNN (IS-SNN) architecture, which removes activation-normalization layers by enforcing signal homeostasis through topology-aware weight standardization and modified residual connections. By folding the standardization operations into static weights offline, IS-SNN removes the runtime statistics tracking and multiplications introduced by activation normalization, restoring an accumulation-oriented inference datapath. Comprehensive experiments show that IS-SNN achieves performance competitive with or superior to computationally expensive dynamic BN techniques across VGG, ResNet, and Transformer-based models. Notably, it achieves a competitive accuracy of 68.05\% on ImageNet and overcomes the severe depth limitations of prior BN-free attempts. Together with a 96.4\% reduction in FPGA lookup table resource consumption for neuron implementations, these results support IS-SNN as a practical framework for building accurate and hardware-friendly deep neuromorphic systems.
R. Ma, Xiaoyang Zhang, J. Bai et al.· arXiv.org· 0 citations
The third generation of neural networks is called Spiking Neural Networks (SNNs) and presents an energy-efficient and physiologically realistic alternative to the traditional Artificial Neural Networks (ANNs). This review discusses the latest advances in SNN models, learning algorithms, and their implementation on neuromorphic hardware. Besides reinforcement learning techniques such as REINFORCE, supervised techniques, such as SpikeProp and unsupervised Spike-Timing-Dependent Plasticity (STDP), are considered in the context of reward-based learning. The performance of the neurological systems technologies, including the IBM TrueNorth, the FPGA-based, and the Intel Loihi, is checked. Despite significant improvement in parallel processing and energy usage in these systems, there are still problems with accuracy of the synapses, training complexity, and scalability. FPGA-based systems are promising in real-time processing, though they have programmability issues that limit their deployment. The article outlines the key limitation of SNN models, namely their inability to handle large datasets and difficult learning tasks. To advance the performance of neuromorphic systems, research is underway on hybrid SNN-ANN models and memristor-based synaptic storage. This review is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems.
Mesala Sravani, K. Kumari, S. M. Reddy· International Journal of Unc...· 0 citations
This work introduces a Temporal Feedback Coding (TFC) scheme that leverages feedback at the encoding stage to diversify spike patterns and designs a Global-Local Dynamic LIF (GLD-LIF) neuron that enhances cross-step dependency modeling by integrating local aggregation and global initialization.
Spiking neural networks (SNNs), characterized by bio-inspired neuronal dynamics and event-driven communication, have attained significant progress in recent years. Nevertheless, training deep SNNs remains challenging due to spatiotemporal information loss and gradient mismatching. To simultaneously address these issues, we propose a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms. We then develop the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics. We demonstrate the advantages of the NGN method through theoretical analysis and experimental validation on CIFAR-10, CIFAR-100, Tiny-ImageNet, DVS-Gesture, N-Caltech101, and CIFAR10-DVS. The proposed approach achieves an accuracy of 87.35% on CIFAR10-DVS within 10 inference time steps. These results support NGN as a practical approach to high-performance neuromorphic computing.
Yajie Zhai, Yanmei Kang, Meng Li et al.· 0 citations
Spiking Neural Networks (SNNs) enable event-driven computation with sparse activations, but building multimodal Transformers on SNNs is hindered by unstable training in deep spiking stacks and the mismatch between dense softmax attention and spike-based communication. We propose SMM Transformer, an SNN-based multimodal Transformer framework that combines (i)PLMP, a Parallel LIF with Multistage Learnable Parameters neuron and a tailored P-STBP algorithm for stable deep SNN training, (ii) SMSA, an attention-inspired spike-driven token-mixing module that replaces dense pairwise softmax attention with channel-wise spike co-activation and self-compensation, and (iii)SMoE, a spiking mixture-of-experts module for modality-aware fusion. Across visual and multimodal benchmarks, SMM Transformer achieves competitive accuracy compared to ANN baselines. Under a standard MAC/AC arithmetic model, SMSA reduces the estimated operator-level compute energy of the attention module by up to 97%, while whole-model profiling shows more moderate but consistent efficiency gains.
Xiubo Liang, Jinxing Han, Yuke Li et al.· 0 citations