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crossref
F. Alnajjar, K. Murase
2006-05-25T16:26:01Z
置信度 0.70
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
Raju Vidap, Ranjith Kumar Nadialli, Vinod Kumar Teriveedhi, K. Suresh Babu 等
2026
置信度 0.80
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Streaming spiking neural network (SNN) accelerators are widely adopted on edge platforms for their deterministic, low-latency inference. Realizing their full efficiency, however, requires three properties to hold simultaneously: a router-free streaming dataflo…
europepmc
Kuilian Yang, Ahmed M. Eltawil, Khaled Nabil Salama
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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Abstract We propose a new framework for Homeostatic Eligibility-based Reward-Optimised Spiking Neural Network (HERO-SNN) that enables spiking neural networks (SNNs) to achieve an optimal refinement in internal spatiotemporal representations based on task perfo…
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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The topology of spiking neural networks (SNNs) plays an important role in determining their dynamic representation ability, recognition performance, and biological interpretability in speech recognition. However, most existing SNN reservoirs are constructed us…
europepmc
2026
置信度 0.80
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europepmc
Zenan Huang, Bingrui Guo, Hailing Xu, Haojie Ruan 等
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
Qian Liang, Yi Zeng, Menghaoran Tang
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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Abstract The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking activity within these networks underlies the computations that transform sensory inputs into appropriate behavioral responses. In this study, we train …
europepmc
Yuqing Zhu, Chadbourne M. B. Smith, Tarek Jabri, Mufeng Tang 等
2026
置信度 0.80
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This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framew…
europepmc
N. K. Kasabov, A. Yang, Z. Wang, I. Abouhassan 等
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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Objectives The classification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images using Convolutional Neural Networks (CNNs) has achieved expert-level accuracy. However, the computational and memory requirements of CNNs pose a barrier to t…
europepmc
Md Rafsan Hassan, Rejaul Islam Shanto, Umar Hasan, Sifat Momen
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
Tao Zhang, Lanqi He, Dingguo Zhang, Mingyang Li 等
2025-07-15T17:43:10Z
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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Artificial muscles play a key role in the future of humanoid robotics and medical devices, with research on wire-driven joints leading the field. While electric servo motors were once at the forefront, the focus has shifted toward materials that react to chang…
europepmc
Florian-Alexandru Brașoveanu, Mircea Hulea, Adrian Burlacu
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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Spiking neural networks (SNNs), which are the next generation of artificial neural networks (ANNs), offer a closer mimicry to natural neural networks and hold promise for significant improvements in computational efficiency. However, the current SNN model is t…
europepmc
Dengyu Wu, Gaojie Jin, Han Yu, Xinping Yi 等
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
Wujian Ye, Shaozhen Chen, Haoxian Liu, Yijun Liu 等
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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Abstract We investigated the interaction of episodic memory processes with the short-term dynamics of recency effects. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats. In the experime…
europepmc
N. Chrysanthidis, F. Fiebig, A. Lansner, P. Herman
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
Kalkidan Deme Muleta, Bai-Sun Kong
2024-08-19T17:29:40Z
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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Background: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing power. In …
europepmc
Yongqiang Zhang, Haijie Pang, Jinlong Ma, Guilei Ma 等
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
Maryam Sadeghi, Yasser Rezaeiyan, Dario Fernandez Khatiboun, Sherif Eissa 等
2024-08-30T13:38:10Z
置信度 0.80
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Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time ser…
europepmc
Ying Li, Xikang Guan, Wenwei Yue, Yongsheng Huang 等
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
Choongseop Lee, Yuntae Park, Sungmin Yoon, Jiwoon Lee 等
2024-12-02T15:07:23Z
置信度 0.80
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europepmc
Zhantu Liang, Xuhong Fang, Zhanhao Liang, Jian Xiong 等
2024
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
Yang Li, Feifei Zhao, Dongcheng Zhao, Yi Zeng
2024
置信度 0.80
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europepmc
2024
置信度 0.80
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europepmc
2025
置信度 0.80