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Neural Information Processing (BMEN90002)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
Availability | Semester 2 |
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Fees | Look up fees |
AIMS
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 systems 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, and neuroprostheses. Material is reinforced through MATLAB and/or NEURON based laboratories.
INDICATIVE CONTENT
Topics include:
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).
Intended learning outcomes
On completion of this subject the student is expected to:
- Explain and Characterise the structure and function of the nervous system, classifying its primary components and their roles.
- Compute and Apply equilibrium neural properties to understand fundamental neural dynamics.
- Differentiate and Characterise types and properties of synapses, relating them to neural communication.
- Examine and Deconstruct membrane mechanisms to explain how action potentials are generated.
- Interpret and Analyse neural responses using point processes, such as Poisson models, to characterise neural variability.
- Evaluate and Appraise neural processing effectiveness using information theoretic measures to assess information transmission.
- Implement, Analyse, and Compare the input-output characteristics of both simplified and biologically-detailed neural models.
- Apply and Organise principles underlying the analysis of biological neural signals to extract meaningful information.
- Explain and Evaluate mechanisms of learning in the brain and nervous system, critically examining key theories.
- Classify and Contrast higher-order neural pathways and systems, evaluating their roles in complex neural processing.
Generic skills
On completion of this subject, students should have developed the following generic skills:
- Ability to apply knowledge of science and engineering fundamentals.
- Ability to undertake problem identification, formulation, and solution.
- Ability to utilise a systems approach to complex problems and to design and operational performance.
- Ability to conduct an engineering project.
Last updated: 8 November 2024