MSc Cognitive and Computational Neuroscience modules
On this page you can find out about the modules on our MSc Cognitive and Computational Neuroscience.
To learn more about the course, visit the University of Sheffield's online prospectus:
Core modules - all students will study:
Fundamentals of Neuroscience (15 credits)
Module leader: Professor Paul Overton
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.
Computational Neuroscience 1 (15 credits)
Module leader: Dr Robert Schmidt
This module provides an introduction to methods in computational neuroscience from two different, but complementary perspectives. First, a high-level or ‘top-down’ view explores how neurons encode and decode sensory information. Second a ‘bottom-up’ or mechanistic approach looking at single neuron models at different levels of abstraction – from a simple ‘integrate-and-fire’ approximations to full conductance-based compartmental models. Throughout this module, the emphasis is on the use of mathematical and computational models of single neuronal function.
Mathematical Modelling and Research Skills (15 credits)
Module leader: Dr Hannes Saal
This module develops basic skills required to understand and participate in research in computational and cognitive neuroscience. The course begins with a refresher course in reading and writing skills, understanding of quantitative data and basic algebra and calculus. The course moves on to cover more advanced mathematical modelling techniques including matrix algebra, ordinary differential equations and optimisation methods. Programming skills are introduced via the MatLab modelling language. All topics are illustrated by application to concrete modelling examples relevant to contemporary neuroscience.
Systems Neuroscience (15 credits)
Module leader: Dr Chris Martin
The module provides an advanced understanding of the brain's major computational systems and how they have been modelled. Major processing units of the brain (e.g, cerebellum and basal ganglia) will be described and, where appropriate, emphasis will be placed on understanding each of these structures as a series of repeating micro- or macrocircuits. The various strategies adopted for modelling these circuits and their interactions with other brain systems will be presented and their predictions for biology considered.
Computational Neuroscience 2 (15 credits)
Module leader: Dr Stuart Wilson
This module is based on the themes of information theory, Bayes' theorem, and learning algorithms. Information theory places limits on how much Shannon information can be transmitted/received by any communication channel, Bayes' theorem provides a method for interpreting incomplete or noisy information, and learning algorithms provide a mechanism for acquiring/storing/retrieving information about the environment. These three related ideas will be explored in the context of neuronal information processing.
Research Project in Cognitive Neuroscience (75 credits)
Module leader: Dr Stuart Wilson
The module allows students to work on an extended (17 week) 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.
Students will also study two modules in either pathway 1 or pathway 2:
Fundamentals of Cognition (15 credits)
Module leader: Dr Myles Jones
This module provides an overview of the fundamental issues in cognitive neuroscience and its contributory disciplines. Topics include: fundamental issues in cognition (memory, attention, learning, perception, affect), developmental processes from neuroscience, psychology and dynamic systems perspectives, and theoretical approaches including cognitive neuropsychology, symbolic and sub-symbolic modelling, and methodological issues.
Modelling Natural Systems (15 credits)
Module leader: Professor Eleni Vasilaki
This module will provide a practical introduction to techniques used for modelling and simulating dynamic natural systems. Many natural systems can be modelled appropriately using differential equations, or individual based methods. In this module, students will explore and understand both modelling approaches. They will gain knowledge of the assumptions underlying these models, their limitations, and how they are derived. Students will learn how to use MATLAB to simulate and explore the dynamics of computational models, using a variety of examples drawn from both natural systems.
Neuroimaging 1 (15 credits)
Module leader: Dr Liat Levita
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, apsyfter 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 design and efficiency will also be discussed and taught in depth.
Neuroimaging 2 (15 credits)
Module leader: Dr Tim Riley
This module further develops on the foundational material in Neuroimaging 1 and provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the techniques of electrophysiology, EEG, and MEG, 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 content of our courses is reviewed annually to make sure it is up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research, funding changes, professional accreditation requirements, student or employer feedback, outcomes of reviews, and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.
Information last updated: 1 December 2020
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