Introduction to computational neurobiology and clustering
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This module aims to provide students with a comprehensive introduction to MATLAB, a widely-used software package for data analysis. Each 1 hour lecture will introduce the topic, associated functions, and theory and will be followed by a 2 hour lab session with hands-on training and exercises using MATLAB. Weekly homework will help to further consolidate the material. Introduces the theory and practice of neural computation.
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Covers the principles of neurocomputing with artificial neural networks widely used for addressing real-world problems such as classification, regression, pattern recognition, data mining, time-series prediction. We look at supervised and unsupervised learning. We study supervised learning using linear perceptrons, and non-linear models such as probabilistic neural networks, multilayer perceptrons, and radial-basis function networks.
Unsupervised learning is studied using Kohonen networks. We provide contemporary training techniques for all these neural networks, and knowledge and tools for the specification, design, and practical implementation of neural networks. This course provides a broad introduction to machine learning and statistical pattern recognition. The course will also discuss recent applications of machine learning e. Throughout history, nature has been a source of inspiration for scientists and researchers.
Observations, many made accidentally, have been triggering inquisitive minds for centuries. In this module, students will be introduced to various concepts in nature to build an understanding of nature-inspired swarm intelligence and evolutionary computation techniques. Students are then guided through the process of implementation of adaptation of these techniques to apply to various existing real-world problems e. The aim of this module is to provide understanding and skills related to research design and to provide extra support for design aspects of dissertation work.
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Topics include: basic concepts; non-experimental methods; experimental methods; quasi-experimental methods; ethical considerations; experience using online databases and other resources; a seminar on design and statistics as principled argument. You will be evaluating the work presented by a psychologist visiting the Department in the context of the Departmental Seminar Series.
Physical Computing is of increasing interest to artists, musicians, choreographers and other creative practitioners for the creation of novel artworks and also for forms of computational interaction between these objects and people. There are many other applications of Physical Computing, for example in museums, ubiquitous and embedded computing, robotics, engineering control systems and Human Computer Interaction.
A physical environment may be sonic, tangible, tactile, visually dynamic, olfactory or any combination of these. In this module, you will learn how the environment, which is essentially continuous, can be monitored by analogue electrical and mechanical sensors. Computers, however, are digital machines programmed by software. One element which you will focus on, therefore, is the interface between the digital and the analogue.
MSc Computational Cognitive Neuroscience | Goldsmiths, University of London
This study will encompass basic physics, electronics, programming and software engineering. The practical objective of this module is the development of the skills you will need for designing and building interactive physical devices. Areas covered include: behavioural genetic techniques; behavioural genetic research into a range of different topics e.
A machine is artificially intelligent when it manages to perform a task that we thought, until the machine proved capable, required human intelligence. Afterwards, we recalibrate our definition of intelligence. Machine learning, an AI technique, has been around for a while. A special machine learning practice known as Deep Learning is revolutionising AI. It is everywhere - or will be soon. We will learn how to build DL programs - known as models - and train them on huge datasets.
TensorFlow, in turn, is programmed using Keras, a high-level Python library. We will write Keras DL code in Jupiter notebooks and plot graphs with another Python library, matplotlib. Finally, our programs will rely on Python's special library for numerical calculation - Numpy. Download the programme specification , for the intake. If you would like an earlier version of the programme specification, please contact the Quality Office. Please note that due to staff research commitments not all of these modules may be available every year.
First or upper second-class honours degree or equivalent undergraduate degree in a relevant discipline.
Applications will be reviewed on a case-by-case basis. Depending on previous background and experience, applicants may be required to take one or more pre-sessional courses for example in programming, statistics, or maths prior to the start of the programme. These courses will be free to MSc offer holders. We accept a wide range of international qualifications.
Find out more about the qualifications we accept from around the world. If you need assistance with your English language, we offer a range of courses that can help prepare you for postgraduate-level study.
Please note that EU fees are being fixed at the above rate for entry. The fee level will be fixed for the duration of your programme. If your fees are not listed here, please check our postgraduate fees guidance or contact the Fees Office , who can also advise you about how to pay your fees. If you're an international student interested in studying part-time, please contact our Admissions Team to find out if you're eligible.
If you are looking to pay your fees please see our guide to making a payment. In addition to your tuition fees, you'll be responsible for any additional costs associated with your course, such as buying stationery and paying for photocopying. You can find out more about what you need to budget for on our study costs page. There may also be specific additional costs associated with your programme.
This can include things like paying for field trips or specialist materials for your assignments. Please check the programme specification for more information. Find out more about postgraduate fees and explore funding opportunities. If you're applying for funding, you may be subject to an application deadline. Apply now. While studying your programme you will have access to the Goldsmiths Careers Service , who can give you tailored advice according to your own skills and interests.
You can also seek advice from the tutors on your course. You may also choose to extend and deepen your academic study by undertaking a PhD in computational cognitive neuroscience or a related field. She uses electroencephalography EEG , magnetoencephalography MEG and intra-craneal recordings to investigate the brain activity along cortico-basal ganglia-thalamocortical circuits. He focuses on the implementation of biologically-realistic neural network models closely mimicking the structure, connectivity, and physiology of the human cortex.
The MSc Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science. This exciting MSc reflects the broad-ranging and strong neuroscience research profile of our Department, equipping you with a rigorous grounding in the theory and applications of cognitive, clinical, and developmental neuroscience. This unique programme combines music psychology with neuroscience, focusing on both the biological and cognitive aspects of musical behaviour.
Avalanches in a stochastic model of spiking neurons. PLoS computational biology, 6 7 , e Botvinick, M. Short-term memory for serial order: a recurrent neural network model. Psychological review, 2 , Chalmers, D. Facing up to the problem of consciousness. Journal of consciousness studies, 2 3 , Getzels, J.
Creativity and intelligence: Explorations with gifted students. Grossberg, S. Laminar cortical dynamics of cognitive and motor working memory, sequence learning and performance: toward a unified theory of how the cerebral cortex works.