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Image of - Statistical Field Theory For Neural Networks | Paperback
Statistical Field Theory For Neural Networks | Paperback

Statistical Field Theory For Neural Networks | Paperback

by Helias M

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.

Highlights

  • binding-icon

    9783030464431

    ISBN:

  • binding-icon

    Helias M

    Author:

  • binding-icon

    203

    Pages:

  • binding-icon

    210 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2020

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Paperback

    Binding:

₹6062

₹7578

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.

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