Results 21 to 30 of about 48,649 (254)
Synaptic proteins as multi-sensor devices of neurotransmission [PDF]
BMC Neuroscience, 2006 Abstract Neuronal communication is tightly regulated in time and space. Following neuronal activation, an electrical signal triggers neurotransmitter (NT) release at the active zone. The process starts by the signal reaching the synapse followed by a fusion of the synaptic vesicle (SV) and diffusion of the released NT in the synaptic cleft ...
Yanay Chava, Brachya Guy, Linial Michal
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Halide perovskite for low‐power consumption neuromorphic devices
EcoMat, 2021 The rapid emergency of data science, information technology, and artificial intelligence (AI) relies on massive data processing with high computing efficiency and low power consumption.
Itaru Raifuku +9 more
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Atomic Layer Deposited SiOX-Based Resistive Switching Memory for Multi-Level Cell Storage
Metals, 2022 Herein, stable resistive switching characteristics are demonstrated in an atomic-layer-deposited SiOX-based resistive memory device. The thickness and chemical properties of the Pt/SiOX/TaN stack are verified by transmission electron microscopy (TEM) and
Yewon Lee +6 more
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Advanced Electronic Materials, 2023 If the speed of machine learning is to be improved, devices and systems with strong resistances to various types of internal noise, mainly internal thermal noise, are urgently needed. The successful demonstration of a synaptic device is reported based on
Haoqun An +4 more
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Tunable synaptic working memory with volatile memristive devices
Neuromorphic Computing and Engineering, 2023 Abstract Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In particular, the working memory (WM) utilizes mechanisms that span orders of magnitudes of timescales, from milliseconds to seconds or even minutes.
Saverio Ricci +4 more
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Recent progress on optoelectronic synaptic devices [PDF]
SCIENTIA SINICA Informationis, 2020 Neuromorphic (brain-like) computing has great potential to solve the von Neumann bottleneck due to its self-adaptive learning, high-parallel computing capability, and low-power consumption. Realization of neuromorphic computing depends on the development of synaptic devices that mimic biological synapses.
Xiaodong PI +3 more
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Low‐Power Computing with Neuromorphic Engineering
Advanced Intelligent Systems, 2021 The increasing power consumption in the existing computation architecture presents grand challenges for the performance and reliability of very‐large‐scale integrated circuits. Inspired by the characteristics of the human brain for processing complicated
Dingbang Liu, Hao Yu, Yang Chai
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Emulating short-term synaptic dynamics with memristive devices [PDF]
Scientific Reports, 2016 AbstractNeuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity.
Wei, S.L. +5 more
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Advanced Electronic Materials, 2023 In this study, the authors fabricate Sn‐doped 100‐nm thick polycrystalline β‐Ga2O3 synaptic field‐effect transistors (FETs) emulating optical and electrical spike stimulation.
Youngbin Yoon +3 more
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Fast, Energy‐Efficient InGaAs Synaptic Phototransistors on Flexible Substrate
Advanced Electronic Materials, 2023 Photodetectors sensing the short‐wave infrared (SWIR) region have great potential due to their significant advantages in a variety of applications because SWIR light possesses both characteristics of visible light and infrared light.
Tae Soo Kim +6 more
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