Cognitive and Computational Neuroscience MSc

The module provides an introduction to core aspects of contemporary neuroscience, and it will consider the current state of knowledge in the field, central theoretical issues and key practical approaches. Topics that are discussed include: neural signalling, sensation and sensory processing, movement and its central control, the 'changing brain' (development and plasticity in the nervous system) and complex brain functions.

This module starts with a primer on neuroscience and the role of computational neuroscience. The module will cover various modelling approaches, from classic biologically plausible to abstract-level models of neurons. The module will then move to higher levels of modelling approaches, such as neural networks and reinforcement learning. While the module emphasises methodological issues and how models can be built, tested and validated at each level, we will also draw connections to specific brain regions to motivate and illustrate the models.

This module develops practical skills in scientific programming in the context of computational and cognitive neuroscience. The course begins with an introduction to basic programming concepts and visualisation techniques. The rest of the module covers advanced skills relevant to contemporary computational and cognitive neuroscience, such as analysis of neural data and running simulations. Techniques introduced include probabilistic methods, dimensionality reduction, classification, and time series analysis. Emphasis is placed on practical skills developed during lab classes.

Researchers in Cognitive Neuroscience use a range of different methods and techniques to better understand the biological processes underpinning cognition. An understanding of the differences between these methods, and their advantages and disadvantages when addressing different research questions, is critical for being able to understand existing research as well as designing and conducting novel research projects. 

This module provides an introduction to a range of state-of-the-art methods used in cutting-edge cognitive neuroscience research, such as EEG, eye-tracking, and tDCS. The module comprises a mix of lectures introducing each technique, demonstrations where students will gain hands-on exposure to cognitive neuroscience equipment, and seminars where students present a recent scientific article using that method. By the end of the module, students will have acquired the knowledge and understanding of a range of cognitive neuroscience methods, their benefits and pitfalls, and be able to use that gained understanding to critically evaluate published research and design new studies. 

This module concerns inferring and modelling neural and cognitive processes underlying human behaviour using computational means. One part of the module will cover normative models, which allow us to solve problems optimally along with their neural or cognitive representations. The other part of the module will focus on cognitive models, which involve fitting models to behavioural data to estimate latent parameters that are assumed to underlie the data and allow us to make inferences about their properties.

The module allows students to work on an extended research project within computational neuroscience and/or cognitive neuroscience and/or systems neuroscience and/or analysis of brain imaging data. Students will learn and apply appropriate research techniques, analyse and interpret the results, and write up the research findings using recognised journal frameworks. Students will receive guidance and regular feedback from their supervisors. The project culminates in an oral presentation and a written dissertation.

The module provides an overview of the fundamental issues in cognitive neuroscience and its contributory disciplines. The approach taken is in terms of its development over the past 50 years, providing an overview of the key concepts in the information processing approach and in cognitive science, followed by an analysis of the advances that have been made recently using cognitive neuroscience techniques. Topics include: fundamental issues in cognition (memory, attention, learning, language); theoretical approaches including cognitive neuropsychology, symbolic and sub-symbolic modelling; and methodological issues.

This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the functional imaging techniques of electrophysiology, optical methods and calcium imaging, each of which will be introduced in the lecture component of the module. In the associated lab classes, students will gain first-hand experience of analysing and processing data sets arising from these techniques.

The module provides an advanced understanding of the brain's major computational systems and the theoretical or model-driven approaches to research of these topics. Major processing units of the brain will be described and, where appropriate, emphasis will be placed on understanding each of these structures in terms of both their micro- and macro-circuitry. One focus of the module will be to impart an appreciation of how many fundamental questions relating to brain function requires study at a range scales, from single cell to whole brain and behaviour. The various strategies adopted for investigating and modelling brain-circuits, and the consideration of circuits as the defining feature of brain systems, will be presented.

This module provides an overview of neuroimaging techniques and fundamental data analysis methodologies employed, specifically those based around functional magnetic resonance imaging (fMRI). The two aspects of neuroimaging (techniques and data analysis) will be taught over the semester. For neuroimaging techniques, after introducing the physical principles underlying fMRI, a description of fMRI-based methods for mapping brain structure and function will follow. For neuroimaging data analysis, the general linear model methodology will be introduced based on the software SPM (Statistical Parametric Mapping), which is one of the most widely used packages for fMRI data analysis. Issues concerning fMRI experimental de-sign and efficiency will also be discussed and taught in depth.