Results 21 to 30 of about 1,883,087 (288)
Device Physics, Modeling and Simulation of Organic Electrochemical Transistors
In this work, we investigate organic electrochemical transistors (OECTs) as a novel artificial electronic device for the realization of synaptic behavior, bioelectronics, and a variety of applications. A numerical method considering the Poisson-Boltzmann
Malte Koch +7 more
doaj +1 more source
Synaptic metaplasticity with multi-level memristive devices
Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when learning a new one.
D’Agostino, S +7 more
openaire +5 more sources
Novel synaptic memory device for neuromorphic computing [PDF]
This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO₂ material.
MANDAL, S +4 more
openaire +3 more sources
A Multilevel Magnetic Synapse Based on Voltage‐Tuneable Magnetism by Nitrogen Ion Migration
Advanced synaptic devices with simultaneous memory and processor capabilities are envisaged as core elements of neuromorphic computing (NC) for low‐power artificial intelligence.
P. Monalisha +4 more
doaj +1 more source
Synaptic proteins as multi-sensor devices of neurotransmission [PDF]
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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Recent progress on optoelectronic synaptic devices [PDF]
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
openaire +1 more source
MSK1 regulates homeostatic and experience-dependent synaptic plasticity [PDF]
The ability of neurons to modulate synaptic strength underpins synaptic plasticity, learning and memory, and adaptation to sensory experience. Despite the importance of synaptic adaptation in directing, reinforcing, and revising the behavioral response ...
Frenguelli, BrunoG. +37 more
core +1 more source
Halide perovskite for low‐power consumption neuromorphic devices
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
doaj +1 more source
Atomic Layer Deposited SiOX-Based Resistive Switching Memory for Multi-Level Cell Storage
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
doaj +1 more source
Pre-synaptic terminal dynamics in the hippocampus [PDF]
This thesis work dealt with the study of synaptic plasticity in the adult brain. This is an area of intensive investigation because instability of synapses is believed to underlie cognitive processes like learning and memory in the CNS, and adaptation ...
De Paola, Vincenzo
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