Neuronal networks

Xem 1-20 trên 21 kết quả Neuronal networks
  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article New Results of a Class of Two-Neuron Networks with Time-Varying Delays

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Modification of a neuronal network direction using stepwise photo-thermal etching of an agarose architecture

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  • ANN can be seen as a parallel distributed information processing structure; ANN has the ability to learn, recall, and generalize from training data by assigning and adjusting the interconnection weights; the overall function is determined by.

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  • The research of neural networks has experienced several ups and downs in the 20th century. The last resurgence is believed to be initiated by several seminal works of Hopfield and Tank in the 1980s, and this upsurge has persisted for three decades. The Hopfield neural networks, either discrete type or continuous type, are actually recurrent neural networks (RNNs). The hallmark of an RNN, in contrast to feedforward neural networks, is the existence of connections from posterior layer(s) to anterior layer(s) or connections among neurons in the same layer....

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  • The potential value of artificial neural networks (ANNs) as a predictor of malignancy has now been widely recognised. The concept of ANNs dates back to the early part of the 20th century; however, their latest resurrection started in earnest in the 1980s when they were applied to many problems in the areas of pattern recognition, control, and optimisation.

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  • Artificial neural networks are learning machines inspired by the operation of the human brain, and they consist of many artificial neurons connected in parallel. These networks work via non-linear mapping techniques between the inputs and outputs of a model indicative of the operation of a real system. Although introduced over 40 years ago, many wonderful new developments in neural networks have taken place as recently as during the last decade or so.

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  • Recurrent Network có các hidden neuron: ph n t làm tầ ử rễ z-1 được dùng Đầu ra của Neural được feedback về tất cả các Neural. Recurrent Neural Network (RNN) Input: Pattern (thường có nhiều hoặc xuống cấp) Output: Corresponding pattern (hoàn hảo/xét môṭ cách tương đôí la ̀ ko có nhiễu )

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  • A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks A normalised version of the real-time recurrent learning (RTRL) algorithm is introduced. This has been achieved via local linearisation of the RTRL around the current point in the state space of the network. Such an algorithm provides an adaptive learning rate normalised by the L2 norm of the gradient vector at the output neuron. The analysis is general and also covers simpler cases of feedforward networks and linear FIR filters...

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  • Data-Reusing Adaptive Learning Algorithms In this chapter, a class of data-reusing learning algorithms for recurrent neural networks is analysed. This is achieved starting from a case of feedforward neurons, through to the case of networks with feedback, trained with gradient descent learning algorithms. It is shown that the class of data-reusing algorithms outperforms the standard (a priori ) algorithms for nonlinear adaptive filtering in terms of the instantaneous prediction error.

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  • Convergence of Online Learning Algorithms in Neural Networks An analysis of convergence of real-time algorithms for online learning in recurrent neural networks is presented. For convenience, the analysis is focused on the real-time recurrent learning (RTRL) algorithm for a recurrent perceptron. Using the assumption of contractivity of the activation function of a neuron and relaxing the rigid assumptions of the fixed optimal weights of the system, the analysis presented is general and is applicable to a wide range of existing algorithms....

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  • The appearance of digital computers and the development of modern theories of learning and neural processing both occurred at about the same time, during the late 1940s. Since that time, the digital computer has been used as a tool to model individual neurons as well as clusters of neurons, which are called neural networks. A large body of neurophysiological research has accumulated since then. For a good review of this research, see Neural and Brain Modeling by Ronald J. MacGregor [21].

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  • This paper presents SIENA, an event notification service that we have de- signed and implemented to maximize both expressiveness and scalability. In Section 3we describe the service’s formally defined application programming in- terface,which is an extension of the familiar publish/subscribe protocol [Birman 1993]. Several candidate server topologies and protocols are presented in Sec- tion 4.

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  • Account Details: Account Details means your BSB and bank account number, credit card number or customer account number, Customer Registration Number, passwords or security codes. Electronic communication: Message transmitted and/or received by: email, short message service (SMS), multimedia message service (MMS) or instant messaging or WAP. Email: The act of sending a message or messages by electronic means to one or more recipients via a network. Financial Details: Means details in relation to your salary, rent, earnings, expenditure, repayments, account balances.

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  • EURASIP Journal on Applied Signal Processing 2003:7, 620–628 c 2003 Hindawi Publishing Corporation Analog VLSI Circuits for Short-Term Dynamic Synapses Shih-Chii Liu Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland Email: shih@ini.phys.ethz.ch Received 14 May 2002 and in revised form 25 September 2002 Short-term dynamical synapses increase the computational power of neuronal networks.

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  • This book is about how to construct and use computational models of specific parts of the nervous system, such as a neuron, a part of a neuron or a network of neurons. It is designed to be read by people from a wide range of backgrounds from the biological, physical and computational sciences. The word ‘model’ can mean different things in different disciplines, and even researchers in the same field may disagree on the nuances of its meaning.

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  • ncluding some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice.

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  • Entities that currently regulate an element of critical infrastructure that has been defined as higher risk should be responsible for oversight. Enforcement of these standards should be incorporated into already established safety or security reviews. Any element of critical infrastructure that has processes or technology that exceed the established standard should be deemed compliant with the standard.

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  • Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Comparative sequence analysis reveals an intricate network among REST, CREB and miRNA in mediating neuronal gene expression...

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  • Chapter 6 NEURAL NETWORKS. How to Raise Your Financial IQ to Stay Ahead of the Competition. The human brain is composed of hundreds of billions of cells known as neurons, which through their connections to each other relay information from one neuron to another.

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  • Few technologies have been used for such a vast variety of applications as neural networks and fuzzy systems. They have been found to be truely interdisciplinary tools appearing in the fields of economics, business, science, psychology, biology, and engineering to name a few. Based upon the structure of a biological nervous system, artificial neural networks use a number of interconnected simple processing elements (“neurons”) to accomplish complicated classification and function approximation tasks....

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