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http://dx.doi.org/10.3389/fnins.2024.1443121 | DOI Listing |
Biomed Eng Lett
January 2025
Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Republic of Korea.
Robotic systems rely on spatio-temporal information to solve control tasks. With advancements in deep neural networks, reinforcement learning has significantly enhanced the performance of control tasks by leveraging deep learning techniques. However, as deep neural networks grow in complexity, they consume more energy and introduce greater latency.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Vertical organic electrochemical transistors (vOECTs) have received widespread attention in bioelectronics, wearable, and neuromorphic electronics due to their high transconductance (), low driving voltage, and biocompatibility. As key parameters of vOECTs, and switching speed (or transient time, τ) are vital for achieving satisfying performance in various practical applications. Here we employ vOECTs with varying top electrode widths for effective and switching speed modulation.
View Article and Find Full Text PDFAdv Mater
January 2025
Institute of Modern Optics & Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin, 300350, P. R. China.
Memristors enable non-volatile memory and neuromorphic computing. Optical memristors are the fundamental element for programmable photonic integrated circuits due to their high-bandwidth computing, low crosstalk, and minimal power consumption. Here, an optical memristor enabled by a non-volatile electro-optic (EO) effect, where refractive index modulation under zero field is realized by deliberate control of domain alignment in the ferroelectric material Pb(MgNb)O-PbTiO(PMN-PT) is proposed.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an alternative, optical implementations of such processors have been proposed, capitalizing on the intrinsic information-processing capabilities of light.
View Article and Find Full Text PDFLight Sci Appl
January 2025
School of Photovoltaic and Renewable Energy Engineering, University of New South Wales (UNSW Sydney), Kensington, NSW, Australia.
A unique optoelectronic synaptic device has been developed, leveraging the negative photoconductance property of a single-crystal material system called CsCoCl. This device exhibits a simultaneous volatile resistive switching response and sensitivity to optical stimuli, positioning CsCoCl as a promising candidate for optically enhanced neuromorphic applications.
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