← Back to all news
arXiv AI By Xiaoli Liu, Yujie Liang, Jialin Li, Malu Zhang

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

Read the original on arXiv AI →

SpikeMoE introduces a spike-based k‑WTA router that uses lateral inhibition and refractory periods to select the top‑K experts based on discrete spike counts, inspired by hippocampal CA1 competition. The framework combines spiking neural network dynamics with mixture‑of‑experts conditional computation and adds a two‑stage missing‑modality module for robust multimodal processing. Experiments on vision, language, and multimodal tasks show that SpikeMoE matches or surpasses ANN baselines while offering energy‑efficient performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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.

By Prasanna Date, Kevin Zhu, Shruti Kulkarni, Ashish Gautam, Chathika Gunaratne, Robert Patton, Tyler Nitzsche, Ian Mulet, Zachary Johnson-Scott, Addison Helms, Duncan Rowden, Simon Weston, Maryam Parsa, Catherine Schuman, Thomas Potok
benchmarks
More like this →
arXiv Machine Learning
Sep 22

A discrete generative model of neuronal spiking activity on microelectrode arrays

The paper presents a discrete generative model for neuronal spiking activity recorded on microelectrode arrays. It uses a shared vocabulary of spatiotemporal motifs learned by a residual vector‑quantized autoencoder and predicts motif occurrence with a factorized masked transformer. Evaluated on 31 assays from human brain organoids and ex vivo hippocampal tissue, the model achieves superior reconstruction and generation performance compared to baselines and shows that motifs are largely reused across assays.

By Md Sayed Tanveer, Mohammed A. Mostajo-Radji, Ge Wang
llmsnlpefficiency
More like this →
arXiv Machine Learning
Sep 15

Exploring napping paradigm for Recurrent Spiking Neural Networks

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
More like this →