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Abstract We propose a spiking-neural-network (SNN)-based deep reinforcement learning (DRL) method for efficient autonomous locomotion control of a starfish-inspired multi-legged soft robot driven by shape memory alloy (SMA) actuators. Conventional soft-robot c…
europepmc
Daisuke Miki, Hiroto Takigasaki
2026
置信度 0.80
-
crossref
Alex Gower
2026-07-16T05:57:39Z
置信度 0.70
-
crossref
Emi Aoki, Flore Norcéide, Gayathri Boopathy, Charles Thompson 等
2026-05-07T19:51:19Z
置信度 0.70
-
Skin plays an important role in biological organisms perceiving and mediating our interactions with the world [...]
crossref
Zixuan Zhang, Chengkuo Lee
2026-02-26T14:51:29Z
置信度 0.70
-
crossref
Lilian Huang, Xiaokun Yu, Xihong Yu
2026-05-11T08:27:20Z
置信度 0.70
-
crossref
Minsu Kwon, Dongwoo Seo, Taesung Kim
2026-03-11T19:35:46Z
置信度 0.70
-
crossref
Amr Hassan, Eman Azab
2026-06-18T16:33:49Z
置信度 0.70
-
Abstract Thanks to their non-volatile and multi-bit properties, memristors have been extensively used as synaptic weight elements in neuromorphic architectures. However, their use to define and re-program the network connectivity has been overlooked. Here, we …
preprints
Thomas Dalgaty, Filippo Moro, Alessio De Pra, Giacomo Indiveri 等
2021
置信度 0.74
-
crossref
Vijayakumar Kempuraj, C. Lakshmi
2025-09-30T22:32:30Z
置信度 0.70
-
Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are d…
europepmc
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang
2026
置信度 0.80
Neuromorphic engineeringComputer scienceKey (lock)Computer architectureArtificial neural network
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The von Neumann architecture, due to the physical separation between memory and processor, has limited the development of data-intensive applications. Neuromorphic computing technologies inspired by the brain's parallel and event-driven operation mechanisms ha…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
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europepmc
Hoda Fares, Margherita Ronchini, Milad Zamani, Hooman Farkhani 等
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
Le Phuong Long, Vo Thi Kien Hao, Nguyen Thi Nu
2026
置信度 0.80
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ABSTRACT Artificial synaptic devices based on field‐effect transistors (FETs) are essential building blocks for neuromorphic computing systems that emulate the signal processing and learning capabilities of biological neural networks. However, most FET‐based s…
europepmc
Kumar Shrestha, Mohammad Karbalaei Akbari, Alireza Pourvahabi Anbari, Puran Pandey 等
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
Unlike energy-intensive von Neumann systems, the human brain efficiently processes complex spatiotemporal information utilizing slow, dissipative ionic dynamics. To emulate this, ion-modulated oxide-based neuromorphic transistors have emerged as a compelling h…
europepmc
2026
置信度 0.80
-
Inspired by biological neural and sensory systems, the in-memory computing and in-sensor computing paradigms have emerged, which integrate computation with memory and processing with sensor respectively, offering a promising solution to address latency and pow…
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
Liquid metals, particularly gallium-based alloys, uniquely combine fluidic compliance with metallic conductivity, which makes them ideal candidates for biomimetic design. Rather than treating biomimicry as the mere imitation of biological forms, we argue that …
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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The human brain has long served as a blueprint for computation, guiding evolution from early symbolic systems to modern deep learning models. Despite these advances, traditional computing systems remain fundamentally limited in mirroring the remarkable flexibi…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
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
-
europepmc
2026
置信度 0.80
-
Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstra…
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
-
The convergence of neuroscience and artificial intelligence has positioned Spiking Neural Networks (SNNs) as one of the pivotal paradigms for future computing. However, the field faces a theoretical challenge: reconciling the mathematical clarity of static dee…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
The integration of biological and artificial systems promises the effective coupling of living cells with electronic devices. However, to create biomimetic platforms capable of bridging biological with artificial systems, it is necessary to first enhance cell …
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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2D ferroelectric materials have recently emerged as a promising class of atomically thin semiconductors capable of integrating sensing, memory, and computation within a single device. Their unique combination of spontaneous switchable polarization, strong ligh…
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
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
-
Artificial intelligence (AI)-aided electrochemical biosensing is becoming integral parts in numerous scenarios. However, existing systems generally perform algorithms in external signal processing units. The necessity of analog-to-digital conversion and data t…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient neuromorphic computing, particularly when processing asynchronous event streams from dynamic vision sensors (DVSs). However, SNNs often suffer from limited representationa…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
Engineering microbial computers has been a longstanding endeavor in synthetic biology. Like other unconventional computing disciplines, the goal is to bring computation into real-world scenarios. Several potential applications in bioproduction, bioremediation,…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2025
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2025
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
Qixuan Li, Lei Zhang
2026
置信度 0.80
-
Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchrono…
preprints
2022
置信度 0.74
-
preprints
2021
置信度 0.74
-
preprints
2018
置信度 0.74
-
preprints
2023
置信度 0.74
-
crossref
Shawn Fostner, Simon A. Brown
2015-11-23T12:09:06Z
置信度 0.70
-
Abstract Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consum…
crossref
Mutsumi Kimura, Yuki Shibayama, Yasuhiko Nakashima
2022-01-03T19:02:36Z
置信度 0.70
-
crossref
2025-07-11T21:06:55Z
置信度 0.70