Computational neurodynamics is the use of computer models to study the dynamics of large networks of interacting neurons. The rationale behind the field, which lies at the theoretical end of computational neuroscience, is that the languages of dynamical systems and information theory are the right ones to express the underlying principles of nervous system operation. Throughout the module, you will learn the basic principles behind the brain's intelligent behaviour, the computational tools needed to simulate such behaviour, and illuminating connections between biological and artificial intelligent systems. Interdisciplinary at heart, the module will also include some elements of neuroscience, biology, and physics (although no background in these is required).
Upon successful completion of this module you will be able to:
- Derive the core mathematical principles of computational models of single neurons;
- Create and evaluate efficient implementations of said models;
- Combine many low-level models to create large networks of neurons, up to the scale of the whole brain;
- Summarise the main properties of large-scale cognitive architectures;
- Contrast similarities and differences between biological and artificial intelligent systems; and
- Interpret the resulting behaviour of such systems using a variety of measures.
The module will cover a variety of topics in computational neuroscience, including:
- Computational modelling of single neurons.
- Synapses and neuroplasticity (learning).
- Complex networks.
- Dynamical systems theory.
- Computational models of whole-brain activity.
- Information theory and neural coding.
- Large-scale cognitive architectures.
The material will be taught mostly through traditional lectures, backed up by unassessed, formative, problems designed to reinforce your understanding of the material. There will be one or more assessed coursework exercises, possibly involving practical laboratory-based exercises.
An online service will be used as an open discussion forum for the module.
A final written exam will contribute most of the marks, with the rest being contributed by coursework exercises.
Written and verbal feedback will be provided throughout the module. Detailed written feedback will be provided on the coursework. Class-wide feedback will be provided after the exam.