This document describes the course objectives and organisation
Computational Neuroscience aims to understand the mechanisms underlying brain function by building quantitative models. The course is intended to introduce the basic concepts of this area and give details of some of the standard models and approaches. The models to be covered will include single neuron models including the Hodgkin-Huxley model, networks of neurons, synaptic learning rules, spiking networks and gaseous neurotransmission. Additionally, if there is time, we will give some detail on systems levels neuroscience models.
| Andy Philippides | CCNR, BIOLS 3D10 | Email: andrewop | Office hour: Friday 12.30 - 1.30 |
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Classes: 1 2hr lecture/seminar per week, held on Mondays, 9.15 - 11.05 in Pev1 2B13
Remember that the lectures for this (and any other) course really aim to provide you with the minimal essential information on a subject. To get a deeper understanding (and in order to do well in the exams, and in subsequent courses which build on these) you Must read around the subject. The lecture notes below are therefore not an exhaustive resource about the given subjects. They may be modified before each lecture. Also, they may contain typographic errors: please let me know if you find any.
Assessment consists of the following two pieces of work. The first counts as 25% of the mark while the second counts for the remaining 75%.
Below is a list of the lectures together with lecture notes
| Lecture 1 | Introduction to Computational Neuroscience | [Lecture notes (ppt)] | [Lecture notes (html)] |
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| Lecture 2 | Neuronal signalling in real neurons | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 3 | Single neuron models: the Hodgkin-Huxley model | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 4/5 | Single neuron models: Beyond Hodgkin-Huxley | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 6 | Multi-compartmental models of single neurons | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 7 | Networks of neurons | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 8 | Learning Rules 1 | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 9 | Learning Rules 1 | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 10 | Modelling Gaseous Neurotransmission | [Lecture notes (ppt)] | [Lecture notes (html)] |
| Lecture 11 | Example Project and Numerical Integration | [Lecture notes (ppt)] | [Lecture notes (html)] |