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Statistical Field Theory for Neural Networks

2020-08-20 Science
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Release : 2020-08-20
Genre : Science
Kind : eBook
Book Rating : 44X/5 ( reviews)

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Book Synopsis Statistical Field Theory for Neural Networks by : Moritz Helias

Download or read book Statistical Field Theory for Neural Networks written by Moritz Helias. This book was released on 2020-08-20. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.

Statistical Mechanics of Neural Networks

2021
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Release : 2021
Genre :
Kind : eBook
Book Rating : 713/5 ( reviews)

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Book Synopsis Statistical Mechanics of Neural Networks by : Haiping Huang

Download or read book Statistical Mechanics of Neural Networks written by Haiping Huang. This book was released on 2021. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Statistical Mechanics of Neural Networks

2022-01-04 Science
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Release : 2022-01-04
Genre : Science
Kind : eBook
Book Rating : 708/5 ( reviews)

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Book Synopsis Statistical Mechanics of Neural Networks by : Haiping Huang

Download or read book Statistical Mechanics of Neural Networks written by Haiping Huang. This book was released on 2022-01-04. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Neural Network Modeling

2018-02-06 Technology & Engineering
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Release : 2018-02-06
Genre : Technology & Engineering
Kind : eBook
Book Rating : 969/5 ( reviews)

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Book Synopsis Neural Network Modeling by : P. S. Neelakanta

Download or read book Neural Network Modeling written by P. S. Neelakanta. This book was released on 2018-02-06. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Markov Chain Monte Carlo Methods in Quantum Field Theories

2020-04-16 Science
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Release : 2020-04-16
Genre : Science
Kind : eBook
Book Rating : 444/5 ( reviews)

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Book Synopsis Markov Chain Monte Carlo Methods in Quantum Field Theories by : Anosh Joseph

Download or read book Markov Chain Monte Carlo Methods in Quantum Field Theories written by Anosh Joseph. This book was released on 2020-04-16. Available in PDF, EPUB and Kindle. Book excerpt: This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs) and statistical mechanics can be used to apply Markov Chain Monte Carlo (MCMC) methods to investigate strongly coupled QFTs. The overwhelming amount of reliable results coming from the field of lattice quantum chromodynamics stands out as an excellent example of MCMC methods in QFTs in action. MCMC methods have revealed the non-perturbative phase structures, symmetry breaking, and bound states of particles in QFTs. The applications also resulted in new outcomes due to cross-fertilization with research areas such as AdS/CFT correspondence in string theory and condensed matter physics. The book is aimed at advanced undergraduate students and graduate students in physics and applied mathematics, and researchers in MCMC simulations and QFTs. At the end of this book the reader will be able to apply the techniques learned to produce more independent and novel research in the field.

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