ELEN E4011 Computational Neuroscience I: Circuits in the Brain
Course Outline
A rough outline of the course is given below. This
page will be periodically updated.
1. Modeling Biological Neurons
September 10.
Reading: pp. 1-20.
Figures:
chapter 2
1.1 What is Computational Neuroscience?
1.2 Modeling Biological Neurons
2. The Hodgkin-Huxley Neuron
September 17 and 24.
Reading: pp. 22-28 and 32-36. Also,
sections 2.1 and 2.2 on the web.
Figures:
chapter 2
Matlab code: spikes directory
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
October 1.
Reading: pp. 38-44. Also, sections 4.1 and 4.2
on the web.
3.1 The Integrate-and-Fire Neuron
3.2 The Spike Response Model
4. Stimulus Representation and the Neural Code
October 8 and 15.
Reading: -----
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
October 22 and 29.
Reading: -----
5.1 Reformulation and Fast Implementations of the Perfect Recovery Algorithm
5.2 Parallel Algorithms for Time Decoding
6. Synaptic Plasticity and Learning
November 5, 12 and 19.
Reading: pp. .
6.1 Hebbian Models of Synaptic Plasticity
6.2 Spike-Time-Dependent Plasticity
7. Elements of Information Theory and Machine Learning
December 3.
Reading: -----
7.1 Learning with Spiking Models
7.2 Learning Algorithms
8. Network Models and Neural Computation
December 10.
Reading: pp. .
8.1 Computation by Excitatory and Inhibitory Networks
8.2 Wilson-Cowan Cortical Dynamics