Results 291 to 300 of about 2,281,376 (313)
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Surgery, 2000
The present disclosure relates to a neuron for an artificial neural network. The neuron includes: a first dot product engine operative to: receive a first set of weights; receive a set of inputs; and calculate the dot product of the set of inputs and the first set of weights to generate a first dot product engine output.
P J, Drew, J R, Monson
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The present disclosure relates to a neuron for an artificial neural network. The neuron includes: a first dot product engine operative to: receive a first set of weights; receive a set of inputs; and calculate the dot product of the set of inputs and the first set of weights to generate a first dot product engine output.
P J, Drew, J R, Monson
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Neural Computation, 1999
A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN.
A, Krogh, S K, Riis
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A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN.
A, Krogh, S K, Riis
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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.
Samanwoy, Ghosh-Dastidar, Hojjat, Adeli
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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.
Samanwoy, Ghosh-Dastidar, Hojjat, Adeli
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Annual Review of Neuroscience, 2005
Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory ...
Tim P, Vogels +2 more
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Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory ...
Tim P, Vogels +2 more
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Physics in Medicine and Biology, 1991
The author reviews recent developments in neural networks that are of general interest to physicists engaged in biological and biomedical research. Examples are examined of models streamlined for collective computation (McCulloch-Pitts, Little, Hopfield and Cowan-Hopfield models), Neural networks in theoretical neurobiology and synthetic neural ...
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The author reviews recent developments in neural networks that are of general interest to physicists engaged in biological and biomedical research. Examples are examined of models streamlined for collective computation (McCulloch-Pitts, Little, Hopfield and Cowan-Hopfield models), Neural networks in theoretical neurobiology and synthetic neural ...
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Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated ...
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Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated ...
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Mathematical Biosciences, 1975
In this paper, the McCulloch-Pitts model of a neuron is extended to a more general model which allows the activity of a neuron to be a “fuzzy” rather than an “all-or-none” process. The generalized model is called a fuzzy neuron. Some basic properties of fuzzy neural networks as well as their applications to the synthesis of fuzzy automata are ...
Lee, Samuel C., Lee, Edward T.
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In this paper, the McCulloch-Pitts model of a neuron is extended to a more general model which allows the activity of a neuron to be a “fuzzy” rather than an “all-or-none” process. The generalized model is called a fuzzy neuron. Some basic properties of fuzzy neural networks as well as their applications to the synthesis of fuzzy automata are ...
Lee, Samuel C., Lee, Edward T.
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Artificial Neural Networks [PDF]
, 2008 Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.
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IEEE Circuits and Devices Magazine, 1988
Examines the following questions associated with artificial neural networks: why people are interested in artificial neural networks; what artificial neural networks are, from the point of view of electronic circuits, and how they work; how they can be programmed and made to solve particular problems; and whether interesting problems can actually be ...
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Examines the following questions associated with artificial neural networks: why people are interested in artificial neural networks; what artificial neural networks are, from the point of view of electronic circuits, and how they work; how they can be programmed and made to solve particular problems; and whether interesting problems can actually be ...
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1976
Publisher Summary This chapter reviews observations from comparative neuroanatomy to stress a few points relevant to real nerve theories : (a) neuronal wiring may in some cases be very precise (fiber projections in the visual system of the fly); (b) the synaptic junctions may be functionally very diversified (lamina ganglionaris of the fly); (c) some
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Publisher Summary This chapter reviews observations from comparative neuroanatomy to stress a few points relevant to real nerve theories : (a) neuronal wiring may in some cases be very precise (fiber projections in the visual system of the fly); (b) the synaptic junctions may be functionally very diversified (lamina ganglionaris of the fly); (c) some
openaire +3 more sources