Courses taught at Edinburgh
Informatics 1 - Cognitive Science (INF1-CG): A lightweight but wide-ranging introduction into the vast field of Cognitive Science for first year UG students. I teach the second half which covers basic neuroscience and some of the most influential models for sensory systems, learning and memory, and reinforcement learning. The course is suitable both for informatics students (the content from cognitive psychology, linguistics and neuroscience will be new) and students from other fields of cognitive science such as psychology. Students from any other discipline are very welcome, we start with the basics in all areas. The modelling will be new to everyone, and the course includes quite a lot of Python programming. While we will gentry introduce this, A-Level/Advanced Higher computing and maths background is recommended. Without such background, you will have to work harder than for other 20pt courses. I will likely be teaching this again in 2023/24.
Neural Information Processing (NIP): Covers material at the interface of neuroscience and machine learning. Topics span a wide arc: neural encoding and decoding, information theory and optimal coding and neural networks. This course is very maths heavy, be prepared to spend time on derivations! See the course website for lectures and relevant materials. Update June 2022: This course is now replaced by our new course Computational Neuroscience.
Neural Computation (NC, usually together with Peggy Series): A basic introduction into computational and mathematical modelling in neuroscience. this begins at the level of single neurons, and we will gradually work our way up to simplified neuron models and networks. This course does require some maths, in particular differential equations, so be prepared to spend time on this if your maths background is rusty. See the course website for lectures and relevant materials. Update June 2022: This course is now replaced by our new course Computational Neuroscience.
Bioinformatics I (BIO1): This course covers basic modern molecular biology and algorithms used in bioinformatics research. The old course website still has the course materials from 2017 for reference.
Bioinformatics II (BIO2): This builds on Bioinformatics 1, and covers statistical and machine learning approaches in bioinformatics. You can take this course without having done Bio1 if you have a basic understanding of molecular biology. The old course website still has the course materials from the 2017 version of the course.
The handout for the PoN (MSc Neuroscience/Neuroinformatics) lecture “Modelling Synaptic Transmission” is availble here. Over several years, this handout has evolved into a rather comprehensive review, and I will cover only things I deem most interesting (which change every year). Please let me know if there are mistakes or important omissions - this handout is constantly improved and extended, and may even be published as a review one day. Update 27. Apr 2013: the review is out now!
VLSI Electrophysiology: For teaching purposes, I have developed software for Tobi Delbrueck’s VLSI retina chip to visualise spike data and create PSTH’s. More information is here. More recently we have also used Tobi’s DVS chip to demonstrate how our retina functions. I’d be keen to hear about similar projects elsewhere.
Potential Students: I’m happy to supervise UG4, MSc, MRes and PhD projects in computational neuroscience. Feel free to contact me with project ideas and some information about your background if you are interested.