Fundamentals of computational neuroscience:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford, United Kingdom
Oxford University Press
2023
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| Edition: | Third Edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xiii, 396 Seiten Illustrationen, Diagramme |
| ISBN: | 9780192869364 |
Staff View
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Contents 1 BACKGROUND 1 Introduction and outlook 1.1 What is computational neuroscience? 1.2 Organization in the brain 1.3 What is a model? 1.4 Is there a brain theory? 1.5 A computational theory of the brain 3 3 5 18 21 26 2 Scientific programming with Python 2.1 The Python programming environment 2.2 Basic language elements 2.3 Code efficiency and vectorization 32 32 33 40 3 Math and Stats 3.1 Vector and matrix notations 3.2 Distance measures 3.3 The ύ'-function 3.4 Numerical calculus 3.5 Basic probability theory 43 43 45 46 47 53 II NEURONS 4 Neurons and conductance-based models 4.1 Biological background 4.2 Synaptic mechanisms and dendritic processing 4.3 The generation of action potentials: Hodgkin-Huxley 4.4 FitzHugh-Nagumo model 4.5 Neuronal morphologies: compartmental models 5 Integrate-and-fire neurons and population models 5.1 The leaky integrate-and-fire models 5.2 Spike-time variability о 5.3 Advanced integrate-and-fire models 5.4 The neural code and the firing rate hypothesis 5.5 Population dynamics: modelling the average behaviour of neurons 65 65 70 77 90 92 98 98 108 115 117 121
xii I Contents 5.6 6 Networks with non-classical synapses Associators and synaptic plasticity 6.1 6.2 6.3 6.4 6.5 Associative memory and Hebbian learning The physiology and biophysics of synaptic plasticity Mathematical formulation of Hebbian plasticity Synaptic scaling and weight distributions Plasticity with pre-and postsynaptic dynamics 129 133 133 140 146 153 163 III NETWORKS 7 Feed-forward mapping networks 7.1 Deep representational learning 7.2 The perceptron 169 169 172 7.3 7.4 7.5 189 197 205 Convolutional neural networks (CNNs) Probabilistic interpretation of MLPs The anticipating brain 8 Feature maps and competitive population coding 8.1 Competitive feature representations in cortical tissue 8.2 Self-organizing maps 8.3 Dynamic neural field theory 8.4 ‘Path’ integration and the Hebbian trace rule о 8.5 Distributed representation and population coding 214 214 216 223 237 241 9 Recurrent associative networks and episodic memory 9.1 The auto-associative network and the hippocampus 9.2 Point-attractor neural networks (ANN) 9.3 Sparse attractor networks and correlated patterns 9.4 Chaotic networks: a dynamic systems view о 9.5 The Boltzmann Machine 9.6 Re-entry and gated recurrent networks 250 250 255 267 273 281 289 IV SYSTEM-LEVEL MODELS 10 Modular networks and complementary systems 10.1 10.2 10.3 10.4 Modular mapping networks Coupled attractor networks Sequence learning Complementary memory systems 11 Motor Control and Reinforcement Learning 11.1 Motor learning and control 11.2 Classical conditioning and reinforcement learning 303 303 309 314 316 323 323 327
Contents 11.3 Formalization of reinforcement learning 11.4 Deep reinforcement learning 329 345 12 The cognitive brain 12.1 Attentive vision 12.2 An interconnecting workspace hypothesis 12.3 Complementary decision systems 12.4 Probabilistic reasoning: causal models and Bayesian networks 12.5 Structural causal models and learning causality 363 363 368 371 Index 387 374 382 xlii |
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