Convex Optimization of Spiking Neural Networks with Global and Approximation Guarantees

arXiv:2605.08022v3 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have been proposed as energy-efficient alternatives to deep neural networks. Their training is non-convex and involves a non-differentiable spike function, so existing methods offer no guarantee of reaching a global optimum. We address this issue by formulating SNN optimization as an equivalent convex optimization problem. Specifically, we show that the weight-decay-regularized training problem for a class of threshold RNNs, including leaky integrate-and-fire SNNs, has zero duality gap and the same optimal

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