Results 171 to 180 of about 171,847 (214)

Vector Operations in Neural Networks Computations

2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2013
Nonlinearity is an important factor in the biological visual neural networks. Among prominent features of the visual networks, movement detections are carried out in the visual cortex. The visual cortex for the movement detection, consist of two layered networks, called the primary visual cortex (V1), followed by the middle temporal area (MT), in which
Naohiro Ishii, Toshinori Deguchi, Masashi Kawaguchi, Hiroshi Sasaki 0004   +3 more
openaire   +1 more source

Brain, Neural Networks, and Computation

Reviews of Modern Physics, 1999
The method by which brain produces mind has for centuries been discussed in terms of the most complex engineering and science metaphors of the day. Descartes described mind in terms of interacting vortices. Psychologists have metaphorized memory in terms of paths or traces worn in a landscape, a geological record of our experiences.
openaire   +1 more source

Neural Networks and Computer Vision

Hand Clinics
Since the conception of the artificial neuron in 1943, neural networks have developed into multi-layer models enabling image recognition, speech recognition, personalized recommendation for web browsing, social media content, and virtual assistants. Harnessing this power, researchers have developed models that can potentially improve both access to ...
Alfred P, Yoon, Kevin C, Chung
openaire   +2 more sources

Neural network for computing GSVD and RSVD

Neurocomputing, 2021
Abstract This paper presents the neural dynamical network to compute the generalized and restricted singular value decompositions (GSVD/RSVD) in the regularization methods for ill-posed problems. The neural network model is defined by ordinary differential equations (ODE) which can be solved by many state-of-the-art techniques.
Liping Zhang 0005, Yimin Wei 0001, Eric King-Wah Chu   +2 more
openaire   +1 more source

Computing with Neural Networks

Science, 1987
T, Kohonen, E, Oja
  +9 more sources

Neural networks for computational neuroscience. [PDF]

, 2008
Computational neuroscience is an appealing interdisciplinary domain, at the interface between biology and computer science. It aims at understanding the experimental data obtained in neuroscience using several different kinds of models, one of which being artificial neural networks.
Meunier, David, Paugam-Moisy, Hélène
openaire   +1 more source

Computation with Infinite Neural Networks

Neural Computation, 1998
For neural networks with a wide class of weight priors, it can be shown that in the limit of an infinite number of hidden units, the prior over functions tends to a gaussian process. In this article, analytic forms are derived for the covariance function of the gaussian processes corresponding to networks with sigmoidal and gaussian hidden units. This
openaire   +1 more source

On the computational power of neural networks and neural automata

1990 IJCNN International Joint Conference on Neural Networks, 1990
The problem of which functions can be computed by a neural network is considered. The answers to this question determine the capabilities and limitations of a neural network as a general-purpose computer. A computation process is defined as the dynamic motion of input states to output states.
openaire   +1 more source

Computing with neural networks

IEEE Potentials, 1993
The resurgence of interest in neural networks is discussed. This interest is prompted by two facts. First, the nervous systems of simple animals can easily solve problems that are very difficult for conventional computers. Second, the ability to model biological nervous system functions using man-made machines increases understanding of that biological
M.N.O. Sadiku, M. Mazzara
openaire   +1 more source

Neural Network Trainer through Computer Networks

2010 24th IEEE International Conference on Advanced Information Networking and Applications, 2010
This paper introduces a neural network training tool through computer networks. The following algorithms, such as neuron by neuron (NBN) [1][2], error back propagation (EBP), Levenberg Marquardt (LM) and its improved versions are implemented in two different computing methods, traditional forward-backward computation and newly developed forward-only ...
Nam Pham, Hao Yu, Bogdan M. Wilamowski
openaire   +1 more source