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  • 1
    Book
    Book
    Oxford [u.a.] : Oxford Univ. Press
    UID:
    b3kat_BV014294016
    Format: XVI, 338 S. , Ill., graph. Darst.
    Edition: 1. publ.
    ISBN: 0198515839 , 0198515820
    Language: English
    Subjects: Biology , Psychology
    RVK:
    RVK:
    Keywords: Nervennetz ; Modell ; Gehirn ; Modell
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Oxford [u.a.] : Oxford Univ. Press
    UID:
    b3kat_BV035767761
    Format: XXV, 390 Seiten , Ill., graph. Darst.
    Edition: 2. ed.
    ISBN: 9780199568413
    Content: "Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic. The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies. Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book is aimed at those within the brain and cognitive sciences, from graduate level and upwards"--Provided by publisher.
    Language: English
    Subjects: Biology , Psychology
    RVK:
    RVK:
    Keywords: Nervennetz ; Modell ; Gehirn ; Modell
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Book
    Book
    Oxford [u.a.] : Oxford Univ. Press
    UID:
    gbv_340899573
    Format: XVI, 338 S. , Ill., graph. Darst. , 25 cm
    Edition: 1. publ.
    ISBN: 0198515820 , 0198515839
    Content: Machine generated contents note: 1 Introduction 1 -- 11 What is computational neuroscience? 1 -- 12 Domains in computational neuroscience 3 -- 13 What is a model? 6 -- 14 Emergence and adaptation 9 -- 15 From exploration to a theory of the brain 10 -- 2 Neurons and conductance-based models 13 -- 21 Modelling biological neurons 13 -- 22 Neurons are specialized cells 14 -- 23 Basic synaptic mechanisms 16 -- 24 The generation of action potentials: Hodgkin-Huxley -- equations 22 -- 25 Dendritic trees, the propagation of action potentials, -- and compartmental models 29 -- 26 Above and beyond the Hodgkin-Huxley neuron: -- fatigue, bursting, and simplifications - 32 -- 3 Spiking neurons and response variability 38 -- 31 Integrate-and-fire neurons 38 -- 32 The spike-response model 42 -- 33 Spike time variability 44 -- 34 Noise models for IF-neurons 48 -- 4 Neurons in a network 56 -- 41 Organizations of neuronal networks 56 -- 42 Information transmission in networks 65 -- 43 Population dynamics: modelling the average behaviour -- of neurons 72 -- 44 The sigma node 79 -- 45 Networks with nonclassical synapses: the sigma-pi -- node 84 -- 5 Representations and the neural code 89 -- 51 How neurons talk 89 -- 52 Information theory 95 -- 53 Information in spike trains 100 -- 54 Population coding and decoding 107 -- 55 Distributed representation 112 -- 6 Feed-forward mapping networks 120 -- 61 Perception, function representation, and look-up -- tables 120 -- 62 The sigma node as perceptron 125 -- 63 Multilayer mapping networks 130 -- 64 Learning, generalization, and biological interpreta- -- tions 134 -- 65 Self-organizing network architectures and genetic -- algorithms 138 -- 66 Mapping networks with context units 140 -- 67 Probabilistic mapping networks 142 -- 7 Associators and synaptic plasticity 146 -- 71 Associative memory and Hebbian learning 146 -- 72 An example of learning associations 149 -- 73 The biochemical basis of synaptic plasticity 153 -- 74 The temporal structure of Hebbian plasticity: LTP and -- LTD 154 -- 75 Mathematical formulation of Hebbian plasticity 158 -- 76 Weight distributions 161 -- 77 Neuronal response variability, gain control, and -- scaling 165 -- 78 Features of associators and Hebbian learning 170 -- 8 Auto-associative memory and network dynamics 174 -- 81 Short-term memory and reverberating network -- activity 174 -- 82 Long-term memory and auto-associators 176 -- 83 Point-attractor networks: the Grossberg-Hopfield -- model 179 -- 84 The phase diagram and the Grossberg-Hopfield -- model 185 -- 85 Sparse attractor neural networks 190 -- 86 Chaotic networks: a dynamic systems view 197 -- 87 Biologically more realistic variations of attractor -- networks 202 -- 9 Continuous attractor and competitive networks 207 -- 91 Spatial representations and the sense of direction 207 -- 92 Learning with continuous pattern representations 211 -- 93 Asymptotic states and the dynamics of neural fields 215 -- 94 'Path' integration, Hebbian trace rule, and sequence -- learning 222 -- 95 Competitive networks and self-organizing maps 226 -- 10 Supervised learning and rewards systems 233 -- 101 Motor learning and control 233 -- 102 The delta rule 237 -- 103 Generalized delta rules 241 -- 104 Reward learning 246 -- 11 System level organization and coupled networks 254 -- 111 System level anatomy of the brain 254 -- 112 Modular mapping networks 258 -- 113 Coupled attractor networks 263 -- 114 Working memory 268 -- 115 Attentive vision 273 -- 116 An interconnecting workspace hypothesis 279 -- 12 A MATLAB guide to computational neuroscience 284 -- 121 Introduction to the MATLAB programming environ- -- ment 284 -- 122 Spiking neurons and numerical integration in -- MATLAB 290 -- 123 Associators and Hebbian learning 298 -- 124 Recurrent networks and network dynamics 301 -- 125 Continuous attractor neural networks 306 -- 126 Error-back-propagation network 311 -- A Some useful mathematics 316 -- A1 Vector and matrix notations 316 -- A2 Distance measures 318 -- A3 The 6-function 319 -- B Basic probability theory 320 -- B1 Random variables and their probability (density) -- function 320 -- B2 Examples of probability (density) functions 320 -- B3 Cumulative probability (density) function and the -- Gaussian error function 323 -- B4 Moments: mean and variance 324 -- B5 Functions of random variables 325 -- C Numerical integration 327 -- C1 Initial value problem 327 -- C2 Euler method 327 -- C3 Example 328 -- C4 Higher-order methods 328 -- C5 Adaptive Runge-Kutta 331 -- Index 333
    Note: Literaturangaben
    Language: English
    Subjects: Computer Science , Biology , Psychology
    RVK:
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    Keywords: Nervennetz ; Modell ; Gehirn
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  • 4
    Book
    Book
    Oxford : Oxford University Press
    UID:
    b3kat_BV048874502
    Format: viii, 396 Seiten , Illustrationen, Diagramme
    Edition: Third Edition
    ISBN: 9780192869364
    Language: English
    Subjects: Computer Science , Biology , Psychology
    RVK:
    RVK:
    RVK:
    RVK:
    Keywords: Nervennetz ; Modell ; Gehirn
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Book
    Book
    Oxford : Oxford University Press
    UID:
    b3kat_BV046033721
    Format: xi, 247 Seiten , Illustrationen, Diagramme
    Edition: First Edition
    ISBN: 9780198828044
    Note: Hier auch später erschienene, unveränderte Nachdrucke
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen
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  • 6
    Book
    Book
    Oxford : Oxford University Press
    UID:
    gbv_1622591275
    Format: xxv, 390 Seiten , Illustrationen, Diagramme , 25 cm
    Edition: Second edition
    ISBN: 9780199568413 , 0199568413
    Content: "Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic. The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies. Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book is aimed at those within the brain and cognitive sciences, from graduate level and upwards"--Provided by publisher
    Content: "Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic. The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies. Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book is aimed at those within the brain and cognitive sciences, from graduate level and upwards"--Provided by publisher
    Note: Literaturangaben , Introduction -- Basic Nuerons -- Neurons and conductance-based models -- Simplified neuron and population models -- Associators and synaptic plasiticity -- Basic Networks -- Cortical organizations and simple networks -- Feed-forward mapping networks -- Cortical feature maps and competitive population coding -- Recurrent associative networks and episodic memory -- System-Level Models -- Modular networks, motor control, and reinforcement learning -- The cognitive brain -- Some useful mathematics -- Numerical calculus -- Basic probability theory -- Basic information theory -- A brief introduction to MATLAB.
    Additional Edition: Erscheint auch als Online-Ausgabe Trappenberg, Thomas P. Fundamentals of computational neuroscience Oxford : Oxford University Press, 2010 ISBN 9780191029448
    Language: English
    Subjects: Biology , Psychology
    RVK:
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    Keywords: Nervennetz ; Modell ; Künstliche Intelligenz ; Neurowissenschaften ; Gehirn ; Modell
    Library Location Call Number Volume/Issue/Year Availability
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