Neural Computation
| Code | School | Level | Credits | Semesters |
| PSGY4062 | Psychology | 4 | 20 | Spring UK |
- Code
- PSGY4062
- School
- Psychology
- Level
- 4
- Credits
- 20
- Semesters
- Spring UK
Summary
The aim is to teach students how neural processes can be understood in computational terms and can be analysed using mathematical and computational methods.
Content:
- Biophysical and reduced models of neurons;
- models of networks (e.g.Hopfield networks, ring-attractors, rate networks);
- models of synaptic platicity and memory;
- perceptrons;
- unserpervised learning;
- neural coding;
- visual system;
- model fitting.
Target Students
MSc Computational Neuroscience, Cognition and AI (compulsory).MSc Machine Learning in Science (choice module).
Classes
- Three 1-hour lectures each week for 10 weeks
30 lecture hours (3 lectures per week), supplemented by weekly tutorial/computer labs of 1-hour.
Assessment
- 30% Coursework: Take-home assignment with exercises, 10-page limit
- 70% Exam (3-hour): Written exam.
Assessed by end of spring semester
Educational Aims
Computational Neuroscience develops computational models of brain functions such as perception, cognition ad memory.The aim is to develop an understanding of computational principles underlying the brain's computations. In particular, students will learn about the biophysicsal underpinning of neural functions, the coding of information in the brain, and plasticity of the nervous system, and how to stimulate these.Learning Outcomes
Intellectual skills
- Describe fundamental principles of computations in the brain, how they can be simulated, analysed and harnessed.
- Identify and compare principles of neural coding of information.
- Identify and compare principles of neural plasticity and learning and their application in AI.
- Identify and use relevant computational tools and modern programming techniques.
Professional / practical skills
- Identify computational strategies to simulate models and analyse data.
- Implement simulations using modern computational tools.
Transferable / key skills
- Effectively communicate their work in writing.
- Analyse and solve complex problems in relevant topics.
- Make effective use of general and specialist computational tools.
Conveners
Last updated 07/01/2025.