153,59 €
180,69 €
-15% su kodu: ENG15
Statistical Field Theory for Neural Networks
153,59 € 
- Išsiųsime per 10–14 d.d.
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.…
153.59
2025-07-13 23:59:00
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- Autorius: Moritz Helias
- Leidėjas: Springer
- ISBN-10: 3030464431
- ISBN-13: 9783030464431
- Formatas: 15.6 x 23.4 x 1.2 cm, minkšti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
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Aprašymas
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.
EXTRA 15 % nuolaida su kodu: ENG15
153,59 €
180,69 €
Išsiųsime per 10–14 d.d.
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Nuolaidos kodas galioja perkant nuo 10 €. Nuolaidos nesumuojamos.
- Autorius: Moritz Helias
- Leidėjas: Springer
- ISBN-10: 3030464431
- ISBN-13: 9783030464431
- Formatas: 15.6 x 23.4 x 1.2 cm, minkšti viršeliai
- Kalba: Anglų
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.
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.
Atsiliepimai