Temporal processing is fundamental for biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking neural networks (SNNs) are well-suited for efficient temporal processing owing to their rich neuronal dynamics and event-driven computa...
Chen-Xiang Ma, Xin-Yi Chen, Yan-Chen Li et al.· IEEE Transactions on Neural...· 0 citations
Stimulus-driven auditory attention determines which sound gains priority when multiple sources compete without an explicit listening goal, yet most computational studies focus either on acoustic salience or on decoding predefined attended targets. This study investigates instruction-free auditory competition using the...
Electroencephalography (EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines and their prospects for further scaling remain unclear. To address these questions, we introduce EEG-Arena, an open-source benchmark covering 30 EEG FMs and 25 supervis...
Zhi-Ge Chen, Shu-Hai Peng, Cheng-Xuan Qin et al.· 0 citations
CausalBN-Bench incorporates background knowledge and training data into prompts to thoroughly unlock LLM'text-comprehension ability during evaluation, whose findings indicate that, LLM understand causality through semantic associations with distinct entities, rather than directly from contextual information or numerica...
Yu Zhou, Xing-Yu Wu, Ji-Bin Wu et al.· IEEE Transactions on Artific...· 22 citations
The proposed parallel multi-compartment spiking neuron (PMSN) presents a promising solution to harness the computational advantages of detailed biological neurons, enabling high-performance and efficient temporal processing on neuromorphic computing systems.
Xinyi Chen, Jibin Wu, Chenxiang Ma et al.· IEEE Transactions on Neural...· 16 citations· ⚡4
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