Results 221 to 230 of about 28,134 (262)

Design of CMOS-memristor hybrid synapse and its application for noise-tolerant memristive spiking neural network. [PDF]

Front Neurosci
Lim JG, Lee SM, Park SJ, Kwak JY, Jeong Y, Kim J, Lee S, Park J, Hwang GW, Lee KS, Park S, Ju BK, Jang HJ, Park JK, Kim I.   +14 more
europepmc   +1 more source

Logic Functionality and Circuit Design of In2Se3‐Based Split‐Gate Ferroelectric Field‐Effect Transistor for Zero‐Trust Applications

Advanced Electronic Materials, EarlyView.
ABSTRACT Van der Waals ferroelectric materials are emerging as key building blocks for future logic devices and integrated circuits. Among them, α‐In2Se3 offers a unique combination of robust room temperature ferroelectricity and semiconducting behavior.
Ankita Ram, Cédric Marchand, Shehr Bano Masood, Aymen Mahmoudi, Takashi Taniguchi, Kenji Watanabe, Bernard Doudin, Abdelkarim Ouerghi, Ian O'Connor, Alexei Gruverman, Jean‐François Dayen   +10 more
wiley   +1 more source

Graph spiking neural network for advanced urban flood risk assessment. [PDF]

iScience
Liang Z, Fang X, Liang Z, Xiong J, Deng F, Nyamasvisva TE.   +5 more
europepmc   +1 more source

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source

Synapses mediate the effects of different types of stress on working memory: a brain-inspired spiking neural network study. [PDF]

Front Cell Neurosci
Du C, Sun Y, Wang J, Zhang Q, Zeng Y.
europepmc   +1 more source

A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network. [PDF]

BMC Neurol
Das P, Nanda S, Panda G, Dash S, Ksibi A, Alsenan S, Bouchelligua W, Mallik S.   +7 more
europepmc   +1 more source

Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme. [PDF]

Commun Eng
Haghighatshoar S, Muir DR.
europepmc   +1 more source

BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction. [PDF]

Front Neurosci
Habara T, Sato T, Awano H.
europepmc   +1 more source

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SPIKING NEURAL NETWORKS

International Journal of Neural Systems, 2009
Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. ANNs have been evolving towards more powerful and more biologically realistic models.
Hojjat Adeli
exaly   +3 more sources

Applications of spiking neural networks

Information Processing Letters, 2005
We are pleased to introduce this issue of Information Processing Letters pre-senting state-of-the-art articles on Applications of Spiking Neural Networks.Spiking neural networks are a class of neural networks that is increasinglyreceiving attention as both a computationally powerful and biologically moreplausible model of distributed computation.
S.M. Bohte (Sander), J.N. Kok (Joost)
openaire   +3 more sources