1. Modeling Biological Neurons
1.1 What is Computational Neuroscience?
1.2 Modeling Biological Neurons
2. The Hodgkin-Huxley Neuron
2.1 Equilibrium Potential
2.2 The Hodgkin-Huxley Equations
2.3 Reduced Hodgkin-Huxley Models
3. Integrate-and-Fire and Other Spiking Neuron Models
3.1 The Integrate-and-Fire Neuron
3.2 The Spike Response Model
4. Stimulus Representation and the Neural Code
4.1 Time Encoding with an Integrate-and-Fire Neuron
4.2 Modeling Visual Receptive Fields in the Retina
4.3 Multichannel Time Encoding and Perfect Recovery
5. Fast Algorithms for Stimulus Recovery
5.1 Reformulation and Fast Implementations of the Perfect Recovery Algorithm
5.2 Parallel Algorithms for Time Decoding
6. Synaptic Plasticity and Learning
6.1 Hebbian Models of Synaptic Plasticity
6.2 Spike-Time-Dependent Plasticity
7. Elements of Information Theory and Machine Learning
7.1 Learning with Spiking Models
7.2 Learning Algorithms
8. Network Models and Neural Computation
8.1 Computation by Excitatory and Inhibitory Networks
8.2 Wilson-Cowan Cortical Dynamics