[2603.24113] Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks

Computer Science > Machine Learning arXiv:2603.24113 (cs) [Submitted on 25 Mar 2026] Title:Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks Authors:Jonathan Haag, Christian Metzner, Dmitrii Zendrikov, Giacomo Indiveri, Benjamin Grewe, Chiara De Luca, Matteo Saponati View a PDF of the paper titled Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks, by Jonathan Haag and 6 other authors View PDF HTML (experimental) Abstract:On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, recent studies show that feedback-control optimizers can enable expressive, on-chip training of neuromorphic devices. In this work, we present a proof-of-concept implementation of such feedback-control optimizers on a mixed-signal neuromorphic processor. We assess the proposed approach in an In-The-Loop(ITL) training setup on both a binary classification task and the nonlinear Yin-Yang problem, demonstrating on-chip training that matches the performance of numerical simulations and gradient-based baselines. Our results highlight the feasibility of feedback-driven, online learning under realistic mixed-signal constraints, and represent a co-design approach toward embedding such rules directly in silicon for autonomous and adaptive neuromorphic computing. Subjects: Machine Learning (cs....