Postgraduate study
Understanding the human brain is one of the grand challenges of science and is important for basic science, health, and technology.
 
  
Qualification
MSc Neural Computational Neuroscience, Cognition and AI
Duration
1 year full-time
Entry requirements
2:1 (or international equivalent)
Other requirements
2:2 (or international equivalent) may be considered provided the applicant has at least one year of relevant work experience or another supporting factor; for quantitatively minded students with a background in psychology, neuroscience, or biosciences as well as those with training in physics, engineering, mathematics, or computer science; no specific biology or computer knowledge required
IELTS
6.5 (no less than 6.0 in any element)

If these grades are not met, English preparatory courses may be available
Start date
September
UK/EU fees
£8,235 - Terms apply
International fees
£21,375 - Terms apply
Campus
University Park
School/department
 

 

Overview

Computational neusroscience

Key facts

  • An interdisciplinary course that uniquely combines aspects of psychology, mathematics and computer science
  • Computational neuroscience aims to better understand brain function, develop better analysis tools for neural data and inspire artificial intelligence algorithms

Recent developments

A number of developments make this course particularly relevant:

  • Recent successes in machine learning are based on analogies with animal brains, i.e. artificial neural networks; these successes have put the analysis of brain computation at the forefront once more
  • In particular study of the biological brain is being considered as a way to resolve some outstanding issues in AI, such as learning with a limited number of samples, generalisation, and the development of explainable AI
  • The study of the brain is currently undergoing a revolution as computational models are becoming powerful and accurate enough to complement experimental approaches
  • Revolutions in experimental recording methods necessitate the use of advanced analysis methods to deal with the enormous volume of data that these methods can produce
 

Full course details

How you'll be taught

Gain a hands-on experience in computational neuroscience research through a blend of traditional courses, individual and group projects.

Emphasis will be on the direct application of the theoretical foundations. You will learn the relevant neuroscience and computer knowledge as the course progresses.

What you'll learn

  • How the brain is believed to work on the cellular, network and systems level
  • How to develop mathematical models of brain function and how to implement them in simulations
  • How cognitive phenomena are related to brain activity
  • Current AI algorithms and how they are based on neuroscience findings
  • An inventory of experimental approaches to measure and analyse brain function

Some topics that will receive particular attention are how:

  • memories are stored and organised in the brain
  • networks of neurons perform computations
  • visual illusions find their origins in neural circuits

Assessment

You will be assessed using exams, coursework, and project reports.

Facilities

Study in a state-of-the-art computing lab.

 
 

Modules

Modules are mainly delivered via lectures and/or problem classes. They take place at University Park throughout the Autumn and Spring semesters. During the Summer semester students undertake an individual research project.

Most module content directly combines insights in brain function with relevant computational approaches. Many modules are supplemented with coursework and tutorials.

Compulsory modules

Computational Cognitive Psychology

This module teaches you cognitive psychology but also how cognition be understood in computational terms, how it can be simulation and how it compares to artificial intelligence approaches.

Topics covered include:cognitive psychology, computational approaches: connectionist networks, deep nets for vision audition and language, memory networks.

 
  • Practical Biomedical Mathematics

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 student-led group activities.

Students will gain experience of applying a variety of mathematical modelling approaches to a range of biomedical problems.

 
  • Machine Learning in Science 1

The purpose of this module is to provide an introduction to the concepts and methods of modern machine learning. It will cover:

  • the basics of supervised learning and unsupervised learning as applied to a variety of problems of linear and non-linear regression
  • classification
  • density estimation
  • data generation

The modules will be a combination of fundamental concepts and hands on application to a selection of example problems.

 
  • Neural Computation

This modules teaches you how neural processes can be understood in computational terms and can be analysed using mathematical and computational methods.

Topics covered include:

  • biophysical and reduced models of neurons
  • models of networks (eg Hopfield networks, ring-attractors, rate networks)
  • models of synaptic plasticity and memory
  • perceptrons
  • unsupervised learning
  • neural coding
  • visual system
  • model fitting
 
Research project
A selection of projects provided by our research academics will be available for you to choose from. You may develop an experimental design or prepare stimuli and run a small study. Alternatively, you may evaluate existing data and interpret the results. 
 

 

Optional modules

You can take 20 credits from the following:

  • Applied Non-Linear Dynamics

This module will cover Nonlinear oscillations, including the linear stability of limit cycles (Floquet theory), the Mathieu equation, and relaxation oscillators (using geometric singular perturbation theory).

The module will conclude with a treatment of Spatially extended systems, covering pattern formation (in both PDE and integral equation models), and weakly nonlinear analysis (amplitude equations and pattern selection).

 
  • Data Analysis for Neuroimaging

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

 
  • Advanced Methods in Psychology
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.
 
  • Analytical Research Methods
A selection of workshops on advanced statistics for the neurosciences.
 
  • The Physics of Deep Learning

This module explores the connections between models of neural networks used for machine learning applications and ideas from physics, in particular from thermal physics and statistical mechanics.

It will discuss the connections between the physics of disordered spin models and NN models of associative memory such as:

  • Hopfield models
  • NN models for unsupervised learning such as Boltzmann machines
  • models of supervised learning such as feed-forward networks
  • the relation between physical coarse-graining and convolutional NNs

It will study the problem of optimisation in rugged energy landscapes and its connection with parameter learning in deep NNs.

You will also cover connections to the physics of stochastic processes, and cover in detail numerical optimisation methods, connecting ideas from stochastic gradient descent to physical methods such as thermal annealing and parallel tempering, and sampling methods such as Monte Carlo.

 
  • Machine Learning in Science 2

The purpose of this module is to cover more advanced topics in machine learning and artificial neural networks following Machine Learning in Science Part 1.

Topics to be covered will include:

  • deep neural networks and deep supervised learning
  • convolutional NNs, RNNs, GANs
  • unsupervised learning, restricted Boltzmann machines, deep RBMs and autoencoders
  • reinforcement learning and Markov decision processes
  • cleaning data and handling large data sets

Concepts will be applied to associated small projects.

 
  • Computer Vision

You'll examine current techniques for the extraction of useful information about a physical situation from individual and sets of images.

You'll cover 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.

 

 

The above is a sample of the typical modules that we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Due to the passage of time between commencement of the course and subsequent years of the course, modules may change due to developments in the curriculum and information is provided for indicative purposes only.

 
 

Fees and funding

See information on how to fund your masters, including our step-by-step guide.

UK/EU Students

The Graduate School website provides more information on internal and external sources of postgraduate funding.

As a student on this course, we do not anticipate any extra significant costs, alongside your tuition fees and living expenses. You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget.

Government loans for masters courses

Masters student loans of up to £10,906 are available for taught and research masters courses. Applicants must ordinarily live in the UK or EU.

International and EU students

Masters scholarships are available for international and EU students from a wide variety of countries and areas of study. You must already have an offer to study at Nottingham to apply. Please note closing dates to ensure you apply for your course with enough time.

Information and advice on funding your degree, living costs and working while you study is available on our website, as well as country-specific resources.

 
 

Careers and professional development

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This course will provide an ideal preparation for a PhD in computational neuroscience, psychology or artificial intelligence.

Other careers include:

  • biomedical modelling
  • artificial intelligence
  • data science
  • brain imaging

Average starting salary and career progression

In 2017, 94.7% of postgraduates in the school who were available for employment had secured work or further study within six months of graduation. The average starting salary was £28,000 with the highest being £40,000.*

* Known destinations of full-time home postgraduates 2016/17. Salaries are calculated based on the median of those in full-time paid employment within the UK.

Career prospects and employability

University of Nottingham is consistently named as one of the most targeted universities by Britain’s leading graduate employers – ranked in the top 10 in The Graduate Market 2013-2019, High Fliers Research.


Those who take up a postgraduate research opportunity with us will not only receive support in terms of close contact with supervisors and specific training related to your area of research, you will also benefit from dedicated careers advice from our Careers and Employability Service.

Our Careers and Employability Service offers a range of services including advice sessions, employer events, recruitment fairs and skills workshops – and once you have graduated, you will have access to the service for life.

 
 
 

Disclaimer
This online prospectus has been drafted in advance of the academic year to which it applies. Every effort has been made to ensure that the information is accurate at the time of publishing, but changes (for example to course content) are likely to occur given the interval between publishing and commencement of the course. It is therefore very important to check this website for any updates before you apply for the course where there has been an interval between you reading this website and applying.

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