← 研踪 PaperTrailarXiv:2611.11053 · v1
Neural Activity Synchronization-Based Fast Local Learnable Spiking Neural Network Architecture
首次提交 2026-11-08 09:13
版本更新 2026-11-08 09:13
雷达观测 2026-11-10 12:16
摘要
Implementing intelligent learning systems on low- power edge devices is challenging due to the high compu- tational complexity, training latency, and energy demands of traditional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs), biologically plausible and spike-driven, come as an alternative. However, SNN training commonly relies on backpropagation through time (BPTT), which is computationally expensive, inherently global, and unsuitable for battery-driven devices. This work proposes a novel local learning framework for multi-layered SNNs inspired by Neural Activity Synchronization (NAS). Each layer updates its own weights using locally com- puted gradients. No gradient backpropagation is needed. This significantly reduces computational complexity, training latency, and energy consumption. The proposed SNN model is trained and evaluated on the MNIST dataset, achieving a classification accuracy of 97.78%. The model is also analyzed with Gaussian and salt-and-pepper noise, demonstrating robust performance across different noise levels.
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v1 · 2026-11-08 09:13 · 最新观测版本
Neural Activity Synchronization-Based Fast Local Learnable Spiking Neural Network Architecture
Implementing intelligent learning systems on low- power edge devices is challenging due to the high compu- tational complexity, training latency, and energy demands of traditional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs), biologically plausible and spike-driven, come as an alternative. However, SNN training commonly relies on backpropagation through time (BPTT), which is computationally expensive, inherently global, and unsuitable for battery-driven devices. This work proposes a novel local learning framework for multi-layered SNNs inspired by Neural Activity Synchronization (NAS). Each layer updates its own weights using locally com- puted gradients. No gradient backpropagation is needed. This significantly reduces computational complexity, training latency, and energy consumption. The proposed SNN model is trained and evaluated on the MNIST dataset, achieving a classification accuracy of 97.78%. The model is also analyzed with Gaussian and salt-and-pepper noise, demonstrating robust performance across different noise levels.
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