Advanced Methods in Bio-inspired AI (983G5)
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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.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A1 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.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 1 hour | 11111111111 |
| Autumn Semester | Seminar | 1 hour | 11111111111 |
| Autumn Semester | Laboratory | 1 hour | 11111111111 |
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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