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
