Systems Neuroscience

Fundamentals of Neuroscience

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.

15 credits

Neural Dynamics and Computation

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.

15 credits

Systems Neuroscience

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.

15 credits

Data Analysis and Visualisation

This module provides basic skills in computational data analysis. Students will learn how to import/export scientific data sets in different formats, how to process and transform them, and how to visualise results. Teaching will be hands-on and computer lab-based. Teaching will focus on the programming language R and associated scientific software. No prior programming experience will be necessary.

15 credits

Neuroanatomy

Knowledge of the neuroanatomy of the central nervous system is fundamental to neuroscience. This module will combine a lecture series on basic and functional neuroanatomy, neuroimaging, peripheral neuroanatomy and basic neuroscience with practical demonstrations of the human brain and spinal cord using models and prosections.  This will enable students to acquire an in-depth and critical understanding of functional neuroanatomy.

15 credits

Research Project in Cognitive Neuroscience

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.

75 credits

Pathway one:

You will learn methods for visualising brain networks, including electrophysiology, optical imaging, and functional magnetic resonance imaging, as well as experimental protocols.

Neuroimaging 1

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.

15 credits

Neuroimaging 2

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.

15 credits

Pathway two:

You will build on your theoretical skills in this area of computational neuroscience before exploring advanced topics including memory, language, and perception at the level of brain networks.

Fundamentals of Cognition

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.

15 credits

Neurocognitive Modelling

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.

15 credits