Results 261 to 270 of about 3,555,912 (299)

Neuromorphic robust framework for integrated estimation and control in dynamical systems using spiking neural networks. [PDF]

Sci Rep
Ahmadvand R, Sharif SS, Banad YM.
europepmc   +1 more source

Hybrid Spike-Encoded Spiking Neural Networks for Real-Time EEG Seizure Detection: A Comparative Benchmark. [PDF]

Biomimetics (Basel)
Mehrabi A, Sreenivasan N, Gunawardana U, Gargiulo G.   +3 more
europepmc   +1 more source

TDE-3: an improved prior for optical flow computation in spiking neural networks. [PDF]

Front Neurosci
Yedutenko M, Paredes-Vallés F, Khacef L, de Croon G.   +3 more
europepmc   +1 more source

A comparative review of deep and spiking neural networks for edge AI neuromorphic circuits. [PDF]

Front Neurosci
M Ferreira P, Wang S, Gao Y, Benlarbi-Delai A.   +3 more
europepmc   +1 more source

Efficient and robust temporal processing with neural oscillations modulated spiking neural networks. [PDF]

Nat Commun
Yan Y, Yang Q, Wu Y, Liu H, Zhang M, Li H, Tan KC, Wu J.   +7 more
europepmc   +1 more source

Review of deep learning models with Spiking Neural Networks for modeling and analysis of multimodal neuroimaging data. [PDF]

Front Neurosci
Khan A, Shim V, Fernandez J, Kasabov NK, Wang A.   +4 more
europepmc   +1 more source

Temporal Hierarchy in Spiking Neural Networks

Moro F, Aceituno PV, Kriener L, Payvand M.   +3 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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   +4 more sources

Fuzzification of Spiked Neural Networks

2008 Second UKSIM European Symposium on Computer Modeling and Simulation, 2008
Biological systems are slow, wide and messy whereas computer systems are fast, deep and precise. Fuzzy neural networks use fuzzy logic to implement higher level reasoning and incorporate expert knowledge into the system while neural networks deal with the low level computational structures capable of learning and adaptation.
David C. Reid, Maybin K. Muyeba
openaire   +1 more source

A spiking recurrent neural network

IEEE Computer Society Annual Symposium on VLSI, 2004
A spiking recurrent neural network implementing an associative memory is proposed. The circuit including four integrate-and-fire (IF) and Willshaw-type binary synapses is designed with the AMI 0.5/spl mu/m CMOS process. A large-scale network is simulated with Matlab and its storage capacity is calculated and analyzed.
Yuan Li, John G. Harris
openaire   +2 more sources