CSE2330 Introduction to Computational Neuroscience

Role of Unit

Prerequisite Knowledge

Unit Objectives

At the completion of the unit student will

1. have knowledge of fundamental computational methods of simulating brain functions and behaviour;
2. understand basic computational principles underlying the operations of neural systems, and the concept of modelling brain functions on various levels, and
3. have appreciation of the problems related to modelling basic brain functions using computing tools
4. be able to use computer software to simulate behaviour of neurons and neural networks

Unit Content

• Basic components of a neural system: neurons, synapses, dendrities, receptive fields
• Hodgkin-Huxley model and its simulation on a computer
• Inter-neuron communication and the principle of synaptic learning
• Fundamentals of Neurobiology. Structure of the brain and brain regions
• Fundamental concepts of data and signal encoding and processing.
• Filtering, approximation and interpolation, clustering, principal component analysis
• Basic neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, recurrent networks.
• Plasticity and Learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning. Self organizing maps.

Recommended Reading

• W.W. Lytton, From Computer to Brain: Foundations of Computational Neuroscience, Springer-Verlag New York Inc., 2002
• E.T Rolls and A. Treves, Neural Networks and Brain Functions, Oxford University Press, 2001
• P. Dayan and L. F. Abbot, Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems, The MIT Press, 2001

Assessment

• Practical works and assignments: 50%
• End of semester exam: 50%

Workload Requirement

• lectures: 2 hours of lectures per week
• practical classes: 2 hours per week
• assignments: an average of 3 hours per week
• self study: 5 hrs a week