Computational Neuroscience, Cognition and AI MSc

This module will provide an introduction to the main concepts and methods of machine learning. It introduces the basics of supervised, unsupervised and reinforcement learning as applied to regression, classification, density estimation, data generation, clustering and optimal control. It will be taught via two sessions per week through a combination of fundamental concepts and hands-on applications.

The aim of this module is to teach you cognitive psychology but also how cognition can be understood in computational terms, simulation and how it compares to AI approaches.

Content includes:

• cognitive psychology
• computational approaches
• connectionist networks
• deep nets for vision audition and language
• memory networks

The aim of this module is to teach you how neural processes can be understood in computational terms and how they can be analysed using mathematical and computational methods.

Topics included:

• biophysical and reduced models of neurons
• models of networks (eg Hopfield networks, ring-attractors and rate networks)
• models of synaptic plasticity and memory
• perceptrons
• unsupervised learning
• neural coding
• visual system
• model fitting

In a typical research project, you will either:

(a) develop an experimental design, prepare stimuli, and to run a study in a small group of subjects, with technical support provided depending on the complexity of the measurement methods, or

(b) evaluate an existing set of, for example, fMRI, MEG, EEG or TMS data and interpret the results

This module involves the application of mathematical modelling to practical problems in biology and medicine, typical of those that mathematicians and systems biologists encounter in academia and industry.

Specific projects are tackled through workshops and group activities. You will gain experience in applying a variety of mathematical modelling approaches to a range of biomedical problems.

Examples include:

• modelling cell signalling pathways using Ordinary Differential Equations
• fitting models to data
• modelling spatial patterning using Partial Differential Equations
• modelling biological tissues using individual-based models
• modelling tissue growth, including cancer.

The course will include training in report writing and giving presentations.

You will examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You will learn a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, image segmentation, pose estimation, recovery of three-dimensional shape and analysis of motion. These problems will be approached with both traditional and modern computer vision approaches, including deep learning.

During this module you will enhance your understanding of nonlinear oscillations, including the linear stability of limit cycles (Floquet theory), the Mathieu equation, and relaxation oscillators (using geometric singular perturbation theory). Synchronisation by periodic forcing will be introduced using the notion of isochrons and phase-response curves, as well as Poincaré sections, circle-maps, mode-locking, and Arnold tongues.

You will explore the treatment of chaos covering tests for chaos (Liapunov exponents and spectral analysis), strange and chaotic attractors, fractal boundaries, and routes to chaos in nonlinear dynamical systems. 

You will also learn about spatially extended systems, covering pattern formation (in both PDE and integral equation models), and weakly nonlinear analysis (amplitude equations and pattern selection).

This module will teach you how to think about artificial neural networks and deep learning from the point of view of physic. It will apply concepts and methods of statistical mechanics to deep learning problems. It will show how a physical science perspective allows you to understand better the workings of neural networks to make them more efficient and expand their range of applications.

The module provides an insight into some more advanced or specialised techniques of data collection, organisation and analysis in psychological research (eg eye-tracking, EEG, fMRI, TMS, computational modeling, diary methodologies and workshops). Lectures will include implementation of analytical procedures in, for example, specialised data management and statistical packages and on specialised data-gathering equipment and software.

This module will cover more advanced topics following from Machine Learning in Science Part 1, specifically the concepts and methods of modern deep learning. Topics include deep neural networks, CNNs, RNNs, GANs, RBMs and deep RBMs, autoencoders, transfer learning, reinforcement learning and Markov decision processes, cleaning data and handling large data sets. The main project for the module is the self-driving PiCar, as seen in this video.

You will cover general research skills and personal development skills. The module includes a number of workshops including presentation and writing skills, careers, understanding the wider context of research, consultancy, and practical and ethical issues, along with appropriate Graduate School courses.

This module enables students to obtain practical research experience, to include a range of activities such as literature searching, study design, ethics, obtaining participants, data collection and analysis, and writing reports. The actual content will depend on the individual Internship.

This module enables students to obtain practical research experience, to include a range of activities such as literature searching, study design, ethics, obtaining participants, data collection and analysis, and writing reports. The actual content will depend on the individual Internship.

Experience a brain imaging session at our on-campus MRI centre. You will then analyse one of the data sets in further lab classes. 

You will be introduced to some of the standard tools used across many labs (including FSL, the FMRIB Software Library from Oxford).

A selection of workshops on advanced statistics for the neurosciences.