Results 231 to 240 of about 1,761,761 (285)
Predicting Liquid Crystal Behavior with Artificial Neural Networks. [PDF]
Micromachines (Basel)Chattha S, Upreti SR, Chan PK.
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Interlayer Dzyaloshinskii-Moriya Interaction in Synthetic Ferrimagnets for Spiking Neural Networks. [PDF]
Adv Sci (Weinh)Li S +14 more
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The Use of Artificial Neural Networks to Model Selected Strength Parameters of the Giant Miscanthus Stalk. [PDF]
Materials (Basel)Francik S +4 more
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Convolutional neural networks: applications, challenges and future prospects in brain tumor research. [PDF]
Front NeurolZhang P, Yang Z.
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2009
Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement.
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Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement.
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Physical Review E, 1993
We propose a class of network models suited for negative-choice classification.
, Nowak, , Lewenstein, , Tarkowski
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We propose a class of network models suited for negative-choice classification.
, Nowak, , Lewenstein, , Tarkowski
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2007
Some of the (comparatively older) numerical results on neural network models obtained by our group are reviewed. These models incorporate synaptic connections constructed by using the Hebb's rule. The dynamics is determined by the internal field which has a weighted contribution from the time delayed signals.
Bikas K, Chakrabarti, Abhik, Basu
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Some of the (comparatively older) numerical results on neural network models obtained by our group are reviewed. These models incorporate synaptic connections constructed by using the Hebb's rule. The dynamics is determined by the internal field which has a weighted contribution from the time delayed signals.
Bikas K, Chakrabarti, Abhik, Basu
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Annual Review of Physiology, 1985
Despite the fact that a large number of neuronal oscillators have been described, there are only a few good examples that illustrate how they operate at the cellular level. For most, there is some isolated information about different aspects of the oscillator network, but too little to explain the whole mechanism.
A I, Selverston, M, Moulins
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Despite the fact that a large number of neuronal oscillators have been described, there are only a few good examples that illustrate how they operate at the cellular level. For most, there is some isolated information about different aspects of the oscillator network, but too little to explain the whole mechanism.
A I, Selverston, M, Moulins
openaire +2 more sources