arXiv:2609.21583v1 Announce Type: cross
Abstract: Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redund...
By Aidin Attar, Michele Rossi
arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.
By Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo, Matthew G. Perich, Guillaume Lajoie
arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.
By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance.
arXiv:2608. 03324v1 Announce Type: new Abstract: Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy.
By Shengyang Li, Yiting Dong, Liuyang Song, Ximing Wang, Luyuan Xie, Cong Li, Qingni Shen, Zhaofei Yu
arXiv:2609.06960v1 Announce Type: new
Abstract: Invasive neural recordings provide high-fidelity measurements of brain activity, with signals such as intracranial EEG (iEEG) and intracortical spiking...
By Ying Chen, Tiou Wang, Zhifeng Yue