COMP 6001 Neuromorphic Algorithms and Computation

Credit Points 10

Legacy Code 800232

Coordinator Saeed Afshar Opens in new window

Description Designing and implementing processing pipelines for event-based sensory data is a crucial skill for neuromorphic engineers to test novel hardware platforms or to develop new algorithms and learning mechanisms. This project-based unit focuses on principles of neuromorphic algorithm design and hardware-friendly neural architecture design for neuromorphic information processors. This unit consists of two streams of research: applied event-based algorithms and bio-inspired spiking networks. Through solving increasingly challenging tasks using distributed, event-based competitive processing elements, students will learn the differences between conventional and neuromorphic algorithm design, critically assessing real-world problems in a structured manner.

School Graduate Research School

Discipline Algorithms

Student Contribution Band HECS Band 2 10cp

Check your HECS Band contribution amount via the Fees page.

Restrictions Must be enrolled in 8124 Master of Applied Neuromorphic Engineering

Learning Outcomes

On successful completion of this subject, students should be able to:
  1. Critically evaluate the advantages and disadvantages of event-based data processing in comparison to Conventional Frame-based data
  2. Assess the fundamental building blocks of neural computation in biology and Neuromorphic Systems
  3. Design and evaluate event-based algorithms on standard von Neumann architectures
  4. Propose novel neuromorphic processing methods relevant to distributed neuromorphic processors
  5. Develop a solution-oriented way of critically assessing real-world problems using Neuromorphic algorithms
  6. Effectively communicate the significance and impact of a specific Neuromorphic system to an audience consisting of both specialist and non-specialists

Subject Content

- Encoding and Processing Conventional and Event-based data
- Architectures of Neural Computation
- Spiking Neural Networks in Biology, Software Simulation and Neuromorphic Hardware
- Event-based Classification
- Event-based Tracking
- Event-based Feature Extraction
- Designing a Novel Event-based Algorithm


The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.

Item Length Percent Threshold Individual/Group Task
Practical Maximum 1000 lines of code 30 N Individual
Practical Maximum 1000 lines of code 30 N Individual
Applied project 1000 words 20 N Group
Viva Voce 15 minutes 20 N Individual

Teaching Periods


Parramatta City - Macquarie St


Subject Contact Saeed Afshar Opens in new window

View timetable Opens in new window