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On Hardware Implementation of DiscreteTime Cellular Neural Networks
"... Cellular Neural Networks are characterized by simplicity of operation. The network consists of a large number of nonlinear processing units; called cells; that are equally spread in the space. Each cell has a simple function (sequence of multiplyadd followed by a single discrimination) that takes a ..."
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Cellular Neural Networks are characterized by simplicity of operation. The network consists of a large number of nonlinear processing units; called cells; that are equally spread in the space. Each cell has a simple function (sequence of multiplyadd followed by a single discrimination) that takes
Image Segmentation based on Active Contours using Discrete Time Cellular Neural Networks
 in Proc. CNNA’98
, 1998
"... In this work we present a new proposal for image segmentation using deformable models, as an application of DiscreteTime Cellular Neural Networks (DTCNN) [i]. This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours are guided ..."
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In this work we present a new proposal for image segmentation using deformable models, as an application of DiscreteTime Cellular Neural Networks (DTCNN) [i]. This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours
Image Segmentation based on Active Contours using Discrete Time Cellular Neural Networks
"... : In this work we present a new proposal for image segmentation using deformable models, as an application of DiscreteTime Cellular Neural Networks (DTCNN) [1]. This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours are guid ..."
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Cited by 1 (0 self)
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: In this work we present a new proposal for image segmentation using deformable models, as an application of DiscreteTime Cellular Neural Networks (DTCNN) [1]. This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours
Article A 181 GOPS AKAZE Accelerator Employing DiscreteTime Cellular Neural Networks for RealTime Feature Extraction
, 2015
"... sensors ..."
Pseudo almost periodic sequence solutions of discrete time cellular neural networks. Nonlinear Analysis: Modelling and Control
, 2009
"... Abstract. In this paper we discuss the existence and uniqueness of a kpseudo almost periodic sequence solutions of a discrete time neural network. We give several sufficient conditions for the exponential and global attractivity of the solution. ..."
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Cited by 2 (1 self)
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Abstract. In this paper we discuss the existence and uniqueness of a kpseudo almost periodic sequence solutions of a discrete time neural network. We give several sufficient conditions for the exponential and global attractivity of the solution.
Evolving Neural Networks through Augmenting Topologies
 Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task ..."
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Cited by 524 (113 self)
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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
, 1989
"... The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precis ..."
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Cited by 529 (4 self)
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the retention of information over time periods having either fixed or indefinite length. 1 Introduction A major problem in connectionist theory is to develop learning algorithms that can tap the full computational power of neural networks. Much progress has been made with feedforward networks, and attention
Learning and development in neural networks: The importance of starting small
 Cognition
, 1993
"... It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time childhood when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language ..."
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Cited by 518 (18 self)
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It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time childhood when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain
Synchronization of Strongly Coupled Neural Networks∗
, 2008
"... Two identical discrete time cellular neural networks are coupled and sharp conditions are found so that some or all neural units will eventually synchronize. In deriving these criteria, we make use of symmetry (invariance) principles, Banach contraction technique and spectral properties of several ..."
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Two identical discrete time cellular neural networks are coupled and sharp conditions are found so that some or all neural units will eventually synchronize. In deriving these criteria, we make use of symmetry (invariance) principles, Banach contraction technique and spectral properties of several
Statistical mechanics of complex networks
 Rev. Mod. Phys
"... Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as ra ..."
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Cited by 2083 (10 self)
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Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled
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