Note: This is an archived Handbook entry from 2014.
|Dates & Locations:|| |
This subject is not offered in 2014.
|Time Commitment:||Contact Hours: 3 hours lecture, one hour tutorial per week and up to 24 hours of workshops. |
Total Time Commitment:
Prerequisite for this subject is:
Study Period Commencement:
|Recommended Background Knowledge:|| |
|Non Allowed Subjects:||
Anti-requisites for this subject are:
BMEN30001(431-336) Neurons: From Action Potential to Learning
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/
Assoc Prof David Grayden
This subject introduces students to the basic mechanisms of information processing and learning in the brain and nervous system. The subject builds upon signals and system modelling approaches to demonstrate the application of mathematical and computation modelling to understanding and simulating neural systems. Aspects of neural modelling that are introduced include: membrane potential, action potentials, neural coding, neural models and neural learning. The application of neural information processing is demonstrated in areas such as: electrophysiology, biomimetics and neuroprostheses. Material is reinforced through MATLAB and/or NEURON based tutorials and laboratories.
Neural information processing analysed using information theoretic measures; generation and propagation of action potentials (spikes); Hodgkin-Huxley equations; coding and transmission of neural information (spiking rate, correlation and synchronisation); neural models (binary, rate based, integrate & fire, Hodgkin-Huxley, and multicompartmental); synaptic plasticity and learning in biological neural systems (synaptic basis of learning, short term, medium term and long term, and rate based Hebbian learning models); spike-timing dependent plasticity (STDP) of synapses; higher order neural pathways and systems (cortical structure and circuits).
This material is reinforced through MATLAB and/or NEURON based tutorials and laboratories
INTENDED LEARNING OUTCOMES (ILO)
On successful completion of this subject, students should be able to:
1. Describe the structure and function of the nervous system;
Hurdle requirement: Students must pass the end of semester examination to pass the subject.
Intended Learning Outcomes (ILOs) 1, 2, 3, 4, 5, 6, 8, 9 and 10 are assessed in the mid-semester test and final written exam. ILOs 2, 5, 6 and 7 are assessed in the submitted laboratory reports.
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
LEARNING AND TEACHING METHODS
The subject is delivered through lectures, tutorials and computer laboratory classes.
INDICATIVE KEY LEARNING RESOURCES
Students are provided with lecture slides, tutorials and worked solutions, laboratory sheets, and reference text lists.
CAREERS / INDUSTRY LINKS
Exposure to neural information processing in industry is provided through research laboratory visits to medical research institutes and guest lectures by representatives of industry, hospitals and research institutes.
Master of Biomedical Engineering |
Master of Philosophy - Engineering
Master of Engineering (Biomedical) |
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