Fundamentals of Computational Neuroscience

Contents/contributors

* Introduction1.1 What is computational Neuroscience?
* 1.2 Domains in Computational Neuroscience
* 1.3 What is a model?
* 1.4 Emergence and adaptation
* 1.5 From exploration to a theory of the brain
* 1.6 Some notes on the book
* Neurons and Conductance-based Models2.1 Modelling biological neurons
* 2.2 Neurons are specialized cells
* 2.3 Basic synaptic mechanisms
* 2.4 The generation of action potentials: Hodgkin-Huxley equations
* 2.5 Dendritic trees, the propagation of action potentials, and compartmental models
* 2.6 Above and Beyond the Hodgkin-Huxley neuron: Fatigue, bursting and simplifications
* Spiking Neurons and response variability3.1 Integrate-and-fire neurons
* 3.2 The spike-response model
* 3.3 Spike time variability
* 3.4 Noise Models for IF neurons
* Neurons in a Network4.1 Organizations of neuronal networks
* 4.2 Information transmission in networks
* 4.3 Population Dynamics: modelling the average behaviour of neurons
* 4.4 The sigma node
* 4.5 Networks with non-classical synapses: the sigma-pi node
* Representations and the neural node5.1 How Neurons talk
* 5.2 Information theory
* 5.3 Information in spike trains
* 5.4 Population coding and decoding
* 5.5 Distributed representation
* Feed-forward mapping networks6.1 Perception, function represntation, and look-up tables
* 6.2 The sigma node as perception
* 6.3 Multi-layer mapping networks
* 6.4 Learning, generalization and biological interpretations
* 6.5 Self-organizing network architectures and geentic algorighms
* 6.6 Mapping networks with context units
* 6.7 Probabilistic mapping networks
* Associators and synaptic plasticity7.1 Associative memory and Hebbian learning
* 7.2 An example of learning association
* 7.3 The biochemical basis of synaptic plasticity
* 7.4 The temporal structure of Hebbian plasticity: LTP and LTD
* 7.5 Mathematical formulation of Hebian plasticity
* 7.6 Weight distributions
* 7.7 Neuronal response variability, gain control, and scaling
* 7.8 Features of associators and Hebbian learning
* Auto-associative memory and network dynamics8.1 Short-term memory and reverberating network activity
* 8.2 Long-term memory and auto-associators
* 8.3 Point attractor networks: The Grossberg-Hopfield model
* 8.4 The phase diagram and the Grossberg-Hopfield model
* 8.5 Sparse attractor neural networks
* 8.6 Chaotic networks: a dynamical systems view
* 8.7 Biologically more realistic variation of attractor networks
* Continuous attractor and competitive networks9.1 Spatial representations and the sense of directions
* 9.2 Learning with continuous pattern representations
* 9.3 Asymptotic states and the dynamics of neural fields
* 9.4 Path-integration, Hebbian trace rule, and sequence learning
* 9.5 Competitive networks and self-organizing maps
* Supervised learning and rewards systems10.1 Motor learning and control
* 10.2 The delta rule
* 10.3 Generalized delta rules
* 10.4 Reward learning
* System level organization and coupled networks111.1 System level anatomy of the brain
* 11.2 Modular mapping networks
* 11.3 Coupled attractor networks
* 11.4 Working memory
* 11.5 Attentive vision
* 11.6 An interconnecting workspace hypothesis
* A MATLAB guide to computational neuroscience12.1 Introduction to hte MATLAB programming environment
* 12.2 Spiking neurons and numerical integration in MATLAB
* 12.3 Associators and Hebbian learning
* 12.4 Recurrent networks and networks dynamics
* 12.5 Continuous attractor neural networks
* 12.6 Error-backpropagation network
* Appendix ASome Useful Mathematics
* Appendix BBasic Probability Theory
* Appendix CNumerical Integration
* Index