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School of Engineering and Informatics (for staff and students)

Advanced Methods in Bio-inspired AI (983G5)

Advanced Methods in Bio-inspired AI

Module 983G5

Module details for 2024/25.

15 credits

FHEQ Level 7 (Masters)

Module Outline

This module will develop your understanding of recent bio-inspired approaches to AI, including their relevance to neuromorphic computing. The benefits, limitations, and open challenges of bio-inspired approaches will be discussed. Key topics include:
• Spiking neural networks including gradient descent with surrogate gradients and exact gradient algorithms.
• Fundamentals of neuromorphic computing approaches.
• Bio-plausible local learning and inference strategies, their benefits and limitations.
• Bio-inspired approaches to unsupervised learning. These topics will be introduced in the context of recent research publications, and you will learn about the latest advances in these topics.

Module learning outcomes

Systematically comprehend the key aspects of recent approaches to bio-inspired AI.

Demonstrate critical awareness of the suitability and challenges of applying bio-inspired methods to a concrete problem

Independently propose, implement and systematically evaluate a bio-inspired AI system.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
ReportA1 Week 1 100.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Autumn SemesterLecture1 hour11111111111
Autumn SemesterSeminar1 hour11111111111
Autumn SemesterLaboratory1 hour11111111111

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Prof Thomas Nowotny

Assess convenor
/profiles/206151

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School of Engineering and Informatics (for staff and students)

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