The human brain is an extremely complex system able to learn to perform a wide range of intelligent behaviours. In this course the brain is considered from the perspective of how to understand a very complex system and also for guidance on to how to design an artificial general intelligence system.
Students will learn how to approach understanding of complex functional systems by means of descriptions on many different levels of detail which can be mapped into each other. These skills are needed to understand, design and modify very complex electronic systems. This approach will be applied to understanding human cognitive phenomena in terms of brain anatomy, physiology and chemistry. Comparisons will be made between learning in the brain and in current artificial neural networks.
The course covers A. How very complex systems can be understood; B. The major human cognitive processes; C. The anatomy, physiology and chemistry of the brain; D. The information process architecture of the brain; E. Differences between the brain and current artificial neural networks. F. Hierarchies of description for understanding cognitive phenomena in terms of brain anatomy, physiology and chemistry.
The course will be relevant to students interested in designing complex functional systems and general artificial intelligence systems. The descriptions of the human brain and approaches to understanding make the course relevant to students interested in research on the mammal brain, and students interested in medical studies of the human brain.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
By the end of this course you should be able to:
- Identify what is necessary to understand a complex system
- Describe the primary information processing functions of major anatomical and physiological structures in the brain.
- Explain the operation of a range of major human memory and other cognitive processes on several different but consistent levels of detail from psychology to physiology.
- Identify ways in which any cognitive process can be understood in terms of physiological and anatomical mechanisms.
- Describe how the brain differs from current artificial neural network applications
Indicative Assessment
- Assignments (30%)
- Mid term exam (10%)
- Final exam (60%)
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Workload
- 30 one hour lectures
- 9 one hour tutorials
Requisite and Incompatibility
Prescribed Texts
Towards a Theoretical Neuroscience: from cell chemistry to cognition by L. Andrew Coward
Majors
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2016 | $3276 |
- International fee paying students
| Year | Fee |
|---|---|
| 2016 | $4368 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 4911 | 20 Feb 2017 | 27 Feb 2017 | 31 Mar 2017 | 26 May 2017 | In Person | N/A |
