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
Keywords:
Nervennetz
;
Modell
;
Gehirn
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