The Department of Psychology (in the School of Psychology, Psychiatry and Psychological Medicine) represented by Prof. Kim T. Ng (HOS), Dr Nellie Georgiou-Karistianis, A/Prof. Stephen Robinson, and the School of Computer Science and Software Engineering represented by Prof. Bala Srinivasan (HOS) and Dr Andrew P. Paplinski decided to introduce a teaching unit aimed at computational models of brain functions and behaviour.

The unit will be primarily targeted at second year Bachelor of Behavioural Neuroscience from the Faculty of Medicine and Bachelor of Computer Science students. It is also expected that the unit will form a part of a proposed degree in Bachelor of Computational Neuroscience to be jointly offered by the Detartment of Psychology and the School of Computer Science and Software Engineering.

It is important to realize that Computational Neuroscience is being offered by many universities world wide as it is a rapidly developing new and exciting area. This unit has been developed and is in accord with similar courses offered by Carnegie Mellon University and the University of Washington.

Initial definition of unit.

The unit will be primarily offered to second year Bachelor of Behavioural Neuroscience and Bachelor of Computer Science students. It is expected that the unit will form a part of a proposed degree in Bachelor of Computational Neuroscience offered in collaboration with the Department of Psychology and the School of Computer Science and Software Engineering.

The unit will be partly a predecessor to the existing CSE5312 Neuro-Fuzzy Computing. In a future Bachelor of Computational Neuroscience degree the unit will be followed by an advanced computational neuroscience unit to be offered at the third year level.

The unit introduces the strategically important link between the School of Computer Science and Software Engineering and the Department Psychology and forges the link between the Faculties of Medicine and IT

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

• 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.

• 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

on-campus

• lectures,
• practical classes,
• assignments

• lectures: A1, A2, A3, A4
• practical classes: A3, A4
• assignments: A1, A2

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

Practical work and assesment: A1, A2, A3, A4. Exam: A1, A2

• 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

Hi-Tech

Students require accesss to a computing lab once a week for 2 supervised hours and 2 unsupervised hours

• one lecturer
• one demonstrator per one practical group

• Software Title:
• Vendor:
• Version:
• OS: MS Windows XP ( ) and/or LINUX ( )
• License Details: (A valid software license certificate/s or Vendor permission must be available upon request if the software is to be installed for teaching)
• Are we currently licensed: Yes ( ) or No ( ) (If unsure, please check with your local IT support )
• If (Yes), who is the contact person for this software?:
• If (No), will the software license be arranged by: Staff member requesting the software ( ) or Technical Services* ( ) ? *If the latter is selected, please discuss your requirement/s with myself ASAP, prior to finalising your request, as there could be financial or resourcing implications.
• Is the software to be installed and accessed from a: Client/Workstation ( ), or Server ( ) ?
• Type of license: Site License ( ), Freeware ( ), FOSS (Free and Open Source Software) ( ), Shareware ( ), Lab based ( ) # of Licenses needed ____
• In what week will you commence using the software in class? Semester 1 (Week ___ ) and/or Semester 2 (Week ___ ):
• Nominated Staff For Acceptance Testing Software: (Name) (Phone) (Email)
• Additional Information: Special Requirements, Software Configuration/Installation Requirements, etc:

Vendor: Mathworks Software Title: MATLAB with

OS: SNG Linux Windows

First Semester Week Required : yes

License Details*: Licensed (contact CSSE TSU)

Nominated Staff for software testing : CSSE TSU

Recommended reading texts will be available from the library.

CSSE 100%

the unit will be taught by CSSE and primarily offered to students in Bachelor of Behavioural Neuroscience (Faculty of Medicine) and in degrees from the Faculty of Information Technology

none