arXiv AI
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
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Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially. This work investigates th...
arXiv Machine Learning
Oct 9
Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks
Local Timescale Gating (LT‑Gate) is a neuron model that tracks fast and slow information in parallel and uses a learned gate to adjust their influence, enabling spiking neurons to preserve long‑term context while responding quickly. The approach includes a variance‑tracking regularization inspired by biological homeostasis and operates with local updates, making it compatible with neuromorphic hardware such as Intel’s Loihi chip. Empirical results show LT‑Gate improves accuracy and retention on sequential learning tasks, achieving about 51 % final accuracy versus 46 % for a recent Hebbian baseline and outperforming prior SNN methods.
arXiv Computer Vision
Aug 28
Parameter Efficient Continual Learning for Sparse Event-Based Transformers
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. sLoTh freezes the backbone and limits plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, updating less than 1% of parameters without replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks while achieving roughly 6.5× lower energy consumption than dense vision transformers.
Hugging Face Trending Papers
Aug 27
Parameter Efficient Continual Learning for Sparse Event-Based Transformers
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. By freezing the backbone and limiting plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, sLoTh adapts to new tasks while updating less than 1% of the parameters and avoiding replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks and achieve roughly 6.5× lower energy consumption than dense vision transformers.
arXiv Computer Vision
Oct 16
The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks
The paper introduces the Dormant Spiking Neuron (DSN), a state-driven mechanism that allows neurons in spiking neural networks to enter a dormant state when their membrane potential falls below a negative threshold. This dynamic gating prunes redundant computations at the instance level and is trained end‑to‑end via backpropagation through time with surrogate gradients. Experiments on static and neuromorphic datasets, including ImageNet, show that DSN reduces computational energy by 40% to over 70% and improves baseline SNN accuracy by 1.34%, outperforming state‑of‑the‑art static pruning methods.
arXiv AI
Jun 19
Hybrid ANN-SNN Pipeline with Local Plasticity
arXiv:2606. 20151v1 Announce Type: cross Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs).