Results 131 to 140 of about 110,849 (310)
Advanced Functional Materials, EarlyView.Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone +8 more
wiley +1 more source
Solution‐Processed Thin‐Film Transistors With Tunable Temporal Dynamics for Neuromorphic Computing
Advanced Functional Materials, EarlyView.Solution‐processed CNT and CNT/P3HT ion‐gated transistors exhibit materials‐defined synaptic timescales: fast CNT devices for high‐frequency spiking and slow hybrid devices for temporal integration. Embedding these dynamics into coupled reservoir‐computing and spiking neural network simulations reveals that a Hybrid‐Reservoir / CNT‐SNN architecture ...
Kevin Schnittker +5 more
wiley +1 more source
Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.Thermally oxidized MoS2‐based radio‐frequency switches enable a multifunctional platform that unifies broadband RF switching and in‐memory computation. The device achieves a cutoff frequency of 33.2 THz with high energy efficiency and supports hardware‐aware signal processing.
Juho Son +5 more
wiley +1 more source
Neural Network architectures design by Cellular Automata evolution [PDF]
, 2000 4th Conference of Systemics Cybernetics and Informatics. Orlando, 23-26 July 2000The design of the architecture is a crucial step in the successful application of a neural network.
Galván, Inés M. +3 more
core
A Graph Neural Networks approach to the state estimation of water distribution systems [PDF]
, 2023 openTraditional Machine Learning cannot deal with graph data in a satisfactory way, in fact they are designed to work with simpler data types like images, which can be represented as grids.
TANCON, GIULIA
core
Advanced Healthcare Materials, EarlyView.ABSTRACT Magnetogenetic deep brain stimulation (MG‐DBS) represents a wireless neuromodulation that has demonstrated long‐lasting behavioral benefits in Parkinson's disease models. However, the circuit‐level mechanisms underlying these therapeutic effects have remained uncharacterized due to limitations of conventional neural interfaces.
Jakyoung Lee +10 more
wiley +1 more source
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.
Bicciato, Alessandro +14 more
core +1 more source
Advanced Healthcare Materials, EarlyView.Human periosteum‐derived cell spheroids bioprinted at high density within a hyaluronic acid matrix promote fusion and hypertrophic cartilage formation in vitro. Early encapsulation enhances spheroid interaction and matrix maturation, generating scalable cartilage templates intended for endochondral bone regeneration.
Ane Albillos Sanchez +6 more
wiley +1 more source
Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review
IEEE AccessElectroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the ...
S. M. Atoar Rahman +6 more
doaj +1 more source