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  3. Neural Information Processing

Neural Information Processing (BMEN90002)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 2
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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

INTENDED LEARNING OUTCOMES (ILOs)


On successful completion of this subject, students should be able to:


1 - describe the structure and function of the nervous system;
2 - calculate equilibrium neural properties;
3 - describe the types and properties of synapses;
4 - describe the membrane mechanisms underlying the generation of action potentials;
5 - interpret neural responses in terms of point processes (Poisson);
6 - evaluate neural processing using information theoretic measures;
7 - implement and analyse the input-output characteristics of simple and biologically-detailed neural models;
8 - describe the principles underlying the analysis of biological neural signals;
9 - describe the mechanisms underlying learning in the brain and nervous system;
10 - describe higher-order neural pathways and systems.

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: 3 November 2022