arXiv AI
Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies
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arXiv AI
Oct 12
Convex Optimization of Spiking Neural Networks with Global and Approximation Guarantees
The paper presents a convex formulation for training spiking neural networks (SNNs), showing that the weight‑decay‑regularized training problem for a class of threshold RNNs—including leaky integrate‑and‑fire SNNs—has zero duality gap and matches a convex program’s optimal value. To handle the exponential size of the resulting convex programs, the authors introduce a randomized surrogate with a high‑probability approximation guarantee. Empirical results demonstrate that the convex variant outperforms surrogate‑gradient training on addition and first‑last‑XOR tasks, matches performance on sequential MNIST, and falls slightly behind a reservoir baseline on base‑3 and base‑5 addition.
arXiv AI
Aug 11
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.
arXiv Machine Learning
Jun 11
A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks
arXiv:2606. 11236v1 Announce Type: cross Abstract: Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients.
arXiv AI
Jun 30
Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
arXiv:2605. 08022v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networks (ANNs).
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 Machine Learning
Jul 17
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.