The Experts below are selected from a list of 53520 Experts worldwide ranked by ideXlab platform
Bernard Widrow - One of the best experts on this subject based on the ideXlab platform.
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nature s Learning rule the hebbian lms algorithm
IEEE International Conference on Cognitive Informatics and Cognitive Computing, 2018Co-Authors: Bernard WidrowAbstract:Hebbian Learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These Learning paradigms are very different. Hebbian Learning is unsupervised. LMS Learning is supervised. However, a form of LMS can be constructed to perform unsupervised Learning and, as such, LMS can be used in a natural way to implement Hebbian Learning. Combining the two paradigms creates a new unsupervised Learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into Learning in living neural networks. A fundamental question is, how does Learning Take Place in living neural networks? “Nature's little secret,” the Learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.
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hebbian Learning and the lms algorithm
IEEE International Conference on Cognitive Informatics and Cognitive Computing, 2016Co-Authors: Bernard WidrowAbstract:Hebbian Learning is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used Learning algorithm. Hebbian Learning is unsupervised. LMS Learning is supervised. However, a form of LMS can be constructed to perform unsupervised Learning and to implement Hebbian Learning. Combining the two paradigms creates a new unsupervised Learning algorithm that has practical engineering applications and provides insight into Learning in living neural networks. A fundamental question is, how does Learning Take Place in living neural networks? The Learning algorithm practiced by nature at the neuron and synapse level may well be the Hebbian-LMS algorithm.
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the hebbian lms Learning algorithm
IEEE Computational Intelligence Magazine, 2015Co-Authors: Bernard Widrow, Youngsik Kim, Dookun ParkAbstract:Abstract-Hebbian Learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, pattern recognition, and artificial neural networks. These are very different Learning paradigms. Hebbian Learning is unsupervised. LMS Learning is supervised. However, a form of LMS can be constructed to perform unsupervised Learning and, as such, LMS can be used in a natural way to implement Hebbian Learning. Combining the two paradigms creates a new unsupervised Learning algorithm that has practical engineering applications and provides insight into Learning in living neural networks. A fundamental question is, how does Learning Take Place in living neural networks? "Nature's little secret," the Learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.
Arash Yazdanbakhsh - One of the best experts on this subject based on the ideXlab platform.
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perception cognition and action in hyperspaces implications on brain plasticity Learning and cognition
Frontiers in Psychology, 2020Co-Authors: Haluk Ogmen, Kazuhisa Shibata, Arash YazdanbakhshAbstract:We live in a three-dimensional (3D) spatial world; however, our retinas receive a pair of 2D projections of the 3D environment. By using multiple cues, such as disparity, motion parallax, perspective, our brains can construct 3D representations of the world from the 2D projections on our retinas. These 3D representations underlie our 3D perceptions of the world and are mapped into our motor systems to generate accurate sensorimotor behaviors. Three-dimensional perceptual and sensorimotor capabilities emerge during development: the physiology of the growing baby changes hence necessitating an ongoing re-adaptation of the mapping between 3D sensory representations and the motor coordinates. This adaptation continues in adulthood and is quite general to successfully deal with joint-space changes (longer arms due to growth), skull and eye size changes (and still being able of accurate eye movements), etc. A fundamental question is whether our brains are inherently limited to 3D representations of the environment because we are living in a 3D world, or alternatively, our brains may have the inherent capability and plasticity of representing arbitrary dimensions; however, 3D representations emerge from the fact that our development and Learning Take Place in a 3D world. Here, we review research related to inherent capabilities and limitations of brain plasticity in terms of its spatial representations and discuss whether with appropriate training, humans can build perceptual and sensorimotor representations of spatial 4D environments, and how the presence or lack of ability of a solid and direct 4D representation can reveal underlying neural representations of space.
Dookun Park - One of the best experts on this subject based on the ideXlab platform.
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the hebbian lms Learning algorithm
IEEE Computational Intelligence Magazine, 2015Co-Authors: Bernard Widrow, Youngsik Kim, Dookun ParkAbstract:Abstract-Hebbian Learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, pattern recognition, and artificial neural networks. These are very different Learning paradigms. Hebbian Learning is unsupervised. LMS Learning is supervised. However, a form of LMS can be constructed to perform unsupervised Learning and, as such, LMS can be used in a natural way to implement Hebbian Learning. Combining the two paradigms creates a new unsupervised Learning algorithm that has practical engineering applications and provides insight into Learning in living neural networks. A fundamental question is, how does Learning Take Place in living neural networks? "Nature's little secret," the Learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.
Katharina Fuglister - One of the best experts on this subject based on the ideXlab platform.
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where does Learning Take Place the role of intergovernmental cooperation in policy diffusion
European Journal of Political Research, 2012Co-Authors: Katharina FuglisterAbstract:Although it is widely accepted that a decentralised system can enhance policy Learning and the spread of best practices, an under-researched question is where that Learning process Takes Place. Using data on the diffusion of health care policies in Switzerland, this article analyses the role of institutionalised intergovernmental cooperation and its impact on the spread of successful policies. The results show that joint membership of policy makers in health policy specific intergovernmental bodies is related to the diffusion of best practices.
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where does Learning Take Place the role of intergovernmental cooperation for policy diusion in switzerland
2009Co-Authors: Katharina FuglisterAbstract:Dear participants of the comparative politics workshop. This paper is part of my on-going dissertation project on policy diusion in federal states. So far, I have analyzed the diusion of a particular policy, namely health insurance subsidy policies, among the sub-national unites of Switzerland. I am now focusing on factors that enhance (or hinder) policy Learning. That is where this paper is situated. I’m grateful for any comments.
Haluk Ogmen - One of the best experts on this subject based on the ideXlab platform.
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perception cognition and action in hyperspaces implications on brain plasticity Learning and cognition
Frontiers in Psychology, 2020Co-Authors: Haluk Ogmen, Kazuhisa Shibata, Arash YazdanbakhshAbstract:We live in a three-dimensional (3D) spatial world; however, our retinas receive a pair of 2D projections of the 3D environment. By using multiple cues, such as disparity, motion parallax, perspective, our brains can construct 3D representations of the world from the 2D projections on our retinas. These 3D representations underlie our 3D perceptions of the world and are mapped into our motor systems to generate accurate sensorimotor behaviors. Three-dimensional perceptual and sensorimotor capabilities emerge during development: the physiology of the growing baby changes hence necessitating an ongoing re-adaptation of the mapping between 3D sensory representations and the motor coordinates. This adaptation continues in adulthood and is quite general to successfully deal with joint-space changes (longer arms due to growth), skull and eye size changes (and still being able of accurate eye movements), etc. A fundamental question is whether our brains are inherently limited to 3D representations of the environment because we are living in a 3D world, or alternatively, our brains may have the inherent capability and plasticity of representing arbitrary dimensions; however, 3D representations emerge from the fact that our development and Learning Take Place in a 3D world. Here, we review research related to inherent capabilities and limitations of brain plasticity in terms of its spatial representations and discuss whether with appropriate training, humans can build perceptual and sensorimotor representations of spatial 4D environments, and how the presence or lack of ability of a solid and direct 4D representation can reveal underlying neural representations of space.