arXiv:2609.08070v1 Announce Type: cross
Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timin...
By Xiangnan Zhang, Jingxin Liu, Ranqi Lu, Jingyu Liu, Qunxi Dong, Fuze Tian, Lixian Zhu, Bin Hu, Bj\"orn W. Schuller
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.
By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara
arXiv:2504.14015v2 Announce Type: replace-cross
Abstract: We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study express...
By Dominik Dold, Philipp Christian Petersen
The paper proposes a biologically inspired micro‑sleep technique called napping for recurrent spiking neural networks, combining proportional weight scaling with continuous stochastic membrane activity. Experiments on an unsupervised SNN trained with trace‑based STDP on Gabor‑preprocessed MNIST show that well‑tuned napping can match the classification accuracy of conventional weight normalization while offering different clustering characteristics. The study suggests that napping may be preferable when representational structure is more important than raw classification speed, despite its higher simulation cost.
By Andreas Massey, Stefano Nichele, Aliaksandr Hubin
The paper introduces Supervised Spike Agreement-Dependent Plasticity (Supervised SADP), a gradient‑free Hebbian learning rule that embeds class labels directly into spike‑driven plasticity. SADP trains output neurons with a supervised Hebbian rule and hidden neurons by measuring Cohen’s kappa agreement with the correct‑class output spike train, optionally aggregating over temporal offsets (K‑shift). Across six benchmark and medical imaging datasets, SADP consistently outperforms reward‑modulated STDP, achieving higher accuracy (e.g., 86.46 % on MNIST) and faster training (up to 2.86× speedup).
By Gouri Lakshmi S, Athira Chandrasekharan, Harshit Kumar, Muhammed Sahad E, Bikas C Das, Saptarshi Bej
arXiv:2606. 03935v1 Announce Type: cross Abstract: The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing.
By Carlo Wenig, Raoul-Martin Memmesheimer, Christian Klos