Cognitive Neuroscience and Human Neuroimaging

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

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

Neuroimaging 1

This module provides an overview of neuroimaging techniques and fundamental data analysis methodolo-gies employed, specifically those based around functional magnetic resonance imaging (fMRI). The two as-pects 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

Neuroimaging 2

This module further develops on the foundational material in PSY6414 (Neuroimaging 1) and provides an overview of neuroimaging techniques and fundamental data analysis methodologies. Specifically, it will focus on the functional imaging 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.

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

Research Project in Cognitive Neuroscience

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.

75 credits

Pathway one:

You will gain an in-depth knowledge of practical neuroanatomy and train in cutting-edge neuroimaging techniques. You'll be introduced to the brain’s major computational systems and how they're modelled.

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 (e.g., the 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. 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

Neuro-Anatomy

Neuropathology is underpinned by knowledge of the neuroanatomy of the central nervous system. This module will combine a lecture series on basic and functional neuroanatomy with laboratory dissection of the human brain and spinal cord, thereby enabling the students to acquire an in-depth and critical understanding of core neuroanatomy. The dissection course will also be complimented by examination of histology specimens to demonstrate the microscopic neuroanatomy and observing a standard brain cut session with a consultant neuropathologist.

15 credits

Pathway two:

You will build on your technical skills in computational neuroscience, exploring advanced mathematical and computational models alongside neuronal information processing.

Computational Neuroscience 1: Biologically Grounded Models

This module starts with a primer on neuroscience and the role of computational neuroscience. The next part of the module covers abstract neuron models and introduce classic computational principles and learning rules related to neural networks. From there we move to more biologically grounded models and deal with single neuron models including leaky-integrate-and-fire and conductance-based neurons. Finally, we examine higher levels of description, in particular systems in context of reinforcement learning. While the emphasis throughout the module is on methodological issues, 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

Computational Neuroscience 2: Theoretical Models

The module builds on ideas developed in Computational Neuroscience 1 to explore networks of neurons, neural circuits and their dynamics, and models of complete brain systems. As in Computational Neuroscience 1, this is taught using both mechanistic (bottom-up) and theoretical (top-down) perspectives but, in this module, emphasis is placed on computational models of neuronal networks and systems. Additional topics will address learning and embodied (robotic) models.

15 credits

Whichever route you choose, this training will develop your transferable skills in critical reading, writing, project management, computational modelling, data analysis and visualisation, and scientific programming using the languages Python and MATLAB.