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Brain, Neural Networks, and Computation
Reviews of Modern Physics, 1999The 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.
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Neural networks for convex hull computation
IEEE Transactions on Neural Networks, 1997Computing convex hull is one of the central problems in various applications of computational geometry. In this paper, a convex hull computing neural network (CHCNN) is developed to solve the related problems in the N-dimensional spaces. The algorithm is based on a two-layered neural network, topologically similar to ART, with a newly developed ...
Y, Leung, J S, Zhang, Z B, Xu
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Biomimetic computations improve neural network robustness
2023AbstractObject recognition by natural and artificial sensory systems requires a combination of selectivity and invariance. Both natural and artificial neural networks achieve selectivity and invariance by propagating sensory information though layers of neurons organised in a functional hierarchy.
Linnea Evanson +4 more
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Vector Operations in Neural Networks Computations
2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2013Nonlinearity 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 +3 more
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2007
This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area.
Tommy W S Chow, Siu-Yeung Cho
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This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area.
Tommy W S Chow, Siu-Yeung Cho
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Neural Network and Neural Computing
Deep learning, a subset of AI, has gained popularity in various fields, including computer vision and NLP. It is based on artificial neural networks, which process multiple layers of data and extract high-level features automatically. Unlike traditional ML algorithms, deep learning can process large unstructured data and complex algorithms better than ...Partha Ghosh, Suradhuni Ghosh
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Future Generation Computer Systems, 1991
Abstract In this paper, we give a general presentation of neural networks, showing their links and differences with Artificial Intelligence and neurosciences. We provide the general formalism of neural networks and describe two neural networks learning algorithms: gradient backpropagation and learning vector quantization.
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Abstract In this paper, we give a general presentation of neural networks, showing their links and differences with Artificial Intelligence and neurosciences. We provide the general formalism of neural networks and describe two neural networks learning algorithms: gradient backpropagation and learning vector quantization.
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Computational Neural Networks — Tools for Spatial Data Analysis
2001The proliferation and dissemination of digital spatial databases, coupled with the ever wider use of Geographic Information Systems [GIS] and Remote Sensing [RS] data, is stimulating increasing interest in spatial analysis from outside the spatial sciences.
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Neural Network Trainer through Computer Networks
2010 24th IEEE International Conference on Advanced Information Networking and Applications, 2010This 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
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