MSc Computational Cognitive Neuroscience

This module covers brain anatomy and functions and modern experimental techniques to study the neural basis of behaviour.

Tutors: Professor Joydeep Bhattacharya and Dr Gianna Cocchini

This module covers the primary statistical analyses used in psychology including:

• multivariate data
• screening and cleaning
• power and sample size determination
• factor analysis
• multiple regression
• analysing contrasts
• univariate and multivariate repeated measures
• ANCOVA and MANCOVA.

This module covers the theory and practice of computational neuroscience, including computational models of neurons, synapses, simple cortical circuits and networks. Students will learn how to implement simple models of biologically-realistic neural systems.

This module aims to explore the neural basis of cognitive functions such as attention, motor control, personality and social cognition. The lectures will cover the basic principles and methods that are used to study the links between brain and behaviour.

In the first week the methods used in Cognitive Neuroscience will be introduced. The lectures will then explore nine topic areas in more detail, guided by three main questions:

1. Which areas in the brain are important for the behaviour studied?
2. When are these areas activated?
3. How are these brain areas causally involved?

Every 2-hour lecture will include approximately 90 minutes of teaching, followed by discussion of a target paper or chapter. For every lecture, a small group of students will read the target paper or book chapter, prepare a short presentation and discussion points, and lead the discussion.

This module covers the fundamental principles of current computational models of cognitive functions and their emergence. This includes perception and attention, working and long-term memory, decision making, and language.

Tutor: Dr Max Garagnani

This module builds on the introductory level of statistics and probability theory and aims to provide students with theory and practice in the application of advanced quantitative methods across multiple areas of psychology and neuroscience.

Topics include: using R and Matlab in psychological research, logistic regression, resampling and Monte Carlo methods, psychological model fitting, Bayesian inference, structural equation modelling, time series analyses, open science tools.

This module introduces programming for Data Science, concentrating primarily on the tools and techniques that are key to achieve results quickly. The module covers current programming languages and environments commonly used in the wider Data Science community, along with ancillary tools and software systems, and gives the student the foundational skills to allow them to develop data-related software for their specific areas of interest.

The knowledge and skills in this course cover the following general areas: Data representations: basic data types, comma-separated variables, Xtensible Markup Language, JavaScript Object Notation, Resource Description Framework, Relations; Data acquisition, storage, retrieval and publication: filesystems, version control, network programming, HTTP, Web servers, relational database systems; Data programming: string processing, numeric vector processing, data frames, scripting and statistical programming; Visualizations: automatically generating charts, graphs, and choropleth maps.

Tutor: Dr. Aaron Gerow

This module aims to provide students with a comprehensive introduction to MATLAB, a widely-used software package for data analysis. The module will start in the Autumn term of 2018 and will be suitable for third year undergraduate students in psychology as well as for students in other programmes.

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.

Tutors: Devin B Terhune and Maria Herrojo Ruiz

Introduces the theory and practice of neural computation. 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.

Tutor: Dr Nikolay Nikolaev

This course provides a broad introduction to machine learning and statistical pattern recognition. The very general topics will include supervised learning (generative/discriminative learning, parametric/non-parametric learning), unsupervised learning (clustering, dimensionality reduction), and learning theory (bias/variance tradeoffs).

The course will also discuss recent applications of machine learning (e.g., in computer vision, or other applications relevant to the research orientation of the department of computing).

Tutor: Dr Jamie Ward

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.g. clustering, medical imaging, optimisation and visualisation).

Tutor: Professor Mark Bishop

To provide understanding and critical skills related to research design and analysis for quantitative and qualitative studies.

Lecture topics will include: psychology as science and the hypothetico-deductive account, basic concepts (e.g., validity, reliability, sampling, and measurement), experimental designs, quasi-experimental and specialised designs, survey design and analysis, single-case designs, introduction to key concepts of qualitative research, thematic approaches and methods of qualitative research (including , thematic analysis, grounded theory and IPA), and discursive approaches to qualitative research (incl. narrative analysis and discourse analysis).

A student-led seminar series, consisting of 10, two-hour seminars on design and statistics as principled argument will run alongside the lectures.

This module encourages students to engage with cutting edge research and to consider this research within the context of wider psychological knowledge. The module seeks to foster skill in evaluating both the content and methods of a particular piece of research and to develop critical thinking concerning research findings.

Students are required to select a research article on any topic of their choice, and to write a critical review of this article. Students are also required to attend as much of the Department’s Invited Speakers’ Series as possible and to identify, in consultation with their supervisor if needs be, the talk of one speaker that will form the focus of an essay. A major part of this process of critical analysis requires that a particular piece of research is considered within a wider psychological context, so it is anticipated that related research would be identified through a search of the relevant literature and by consideration of the background work cited by the speaker during the course of the talk.

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.

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.

This module will provide a systematic introduction to behavioural genetics. Conceptual, historical, theoretical and ethical issues will be discussed alongside developments in specific fields (e.g. behavioural genetics and psychopathology).

The module will promote an understanding of the current state of affairs with regards to behavioural genetics. Basic principles as well as recent developments will be explored in relation to a broad range of phenotypes. Historical and ethical issues will be discussed.

Tutorials:

Tutorial topics for this module are:

1. Major Concepts in Behavioural Genetics. This tutorial will take place in Week 4 and is designed to help you to prepare for the Oral Examination (Week 9) Exact dates/ times TBC
2. Select a psychopathology of your choice and discuss key behavioural genetic findings for this disorder. This tutorial is designed to help students to design and structure a tutorial essay on this topic and generally on writing examined essays. This tutorial will take place in Week 9. You are encouraged to submit one essay on the topic of this tutorial (optional).  The essay can have one of the following formats: (1) a fully referenced essay of between 2,000 and 2,500 words; (2) an essay written under exam conditions (hand-written, 45 minutes, with no notes). Exact dates TBC.

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.

Ai is a broad field and includes many disciplines and ideas. But a new technique is taking over.

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.

Simply, AI ≈ DL.

We will learn how to build DL programs - known as models - and train them on huge datasets. We will be using TensorFlow, Google's important DL resource. 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