Sample Research Training Modules

Please note that these are sample projects and may not all be available in your year of study. Research Training Modules are subject to change and development each year.

Research Module 1

Neuroanatomy and brain histology: from Golgi to Connectomics

Dr Elisa Galliano, Dr Steve Edgley, Dr Leila Muresan, Dr Marta Costa, Dr Andrea Dimitracopoulos (Physiology, Development & Neuroscience & Zoology)

This practical aims to introduce you to the analysis of the microscopic structure of the nervous system using histology, an important research tool in neuroscience that you will have encountered through some of your lecture courses. In this practical, you have the opportunity to learn about classic and cutting-edge staining techniques, examine photographs of brain tissue at various magnifications and watch videos illustrating some of the more advanced ways of analysing microscopic structure and function in specific cell types and circuits. You will then analyse a full confocal microscopy dataset, and attempt to reconstruct a full neuron for the drosophila connectomics project.

Research Module 2

Electrophysiological recordings: how to eavesdrop into neuronal conversations

Dr Elisa Galliano, Dr Hugh Matthews, Dr Ewan St John Smith, Dr Julija Krupic, Dr Mark Burrell (Physiology, Development & Neuroscience & Pharmacology)

This practical builds on the anatomical knowledge of neurons, circuits and synapses gathered from the previous one, to look into the electrophysiological properties of excitable cells. With lectures, online exercises and data analysis, this exciting new virtual lab will cover the various electrophysiological recording configurations (intra and extracellular), in brain slices and in the intact animals, in different neuronal networks.

Research Module 3

Quantitative Analysis of Neuroimaging Studies

Dr Jane Garrison, Department of Psychology

Meta-analysis is an increasingly popular and useful method for integrating research findings across numerous functional neuroimaging studies. Quantitative meta-analyses can be used to localise brain regions most consistently associated with a particular type of behaviour despite variation in participants, tasks and scanning procedures. Students will learn how to extract coordinates of brain activation patterns from neuroimaging papers and use software to conduct a meta-analysis of human brain function.

Research Module 4

Pybrain workshop: The python for brain imaging online workshop series

Dr Johan Carlin, MRC Cognition and Brain Sciences Unit

The python for brain imaging (pybrain) workshop series is for practicing neuroimagers who want to learn more about how to work with data in Python. We assume no previous Python background, and provide a comprehensive hands-on introduction to state of the art libraries for experiment building, statistical modelling, machine learning, and multimodal neuroimaging analysis.

The workshops are hosted by the MRC Cognition and Brain Sciences Unit at the University of Cambridge.

Workshop 1: Introductory python, basic data analysis, experiment coding
Edwin Dalmaijer, MRC CBU

Workshop 2: MRI analysis in Python using Nipype, Nilearn and more
Michael Notter, Lausanne, and Peer Herholz, MNI

Workshop 3: M/EEG analysis with MNE Python
Richard Höchenberger, INRIA

Workshop 4: Diffusion MRI with DIPY
Rafael Henriques, Champalimaud

For more information, please see

For more details, please email Johan Carlin (

Research Module 5

Deep Learning

Professor Stephen Eglen, Applied Maths and Theoretical Physics

The course will cover the basic theory and applications of deep learning (some neuroscience applications, some more general in computational biology). Deep learning lectures will run for 5 sessions, and you can check the website for developments

Research Module 6

Visualisation & quantification of myelinated pathways in the brain

Professor Jeff Dalley and Dr Claudia Pama, Department of Psychology

The speed and fidelity of information transfer in the CNS fundamentally depends on myelinated neural circuits that collectively form the building blocks of functional brain networks. Myelinated circuits are established during critical developmental periods and can be modified by factors such as learning, ageing and various neurological disorders. This hand-on workshop covers the practical steps involved in processing brains for myelin-based immunohistochemistry, including sectioning on a cryostat/vibratome, specific staining with anti-myelin basic protein, and microscopy to visualise specific myelinated projections in the rodent brain.

Research Module 7

Critique of null hypothesis significance testing from the Bayesian perspective

Dr Dénes Szűcs, Department of Psychology

Dr Dénes Szűcs will run an 8-hour course. It will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics, which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

Prerequisites: You should have studied some basic statistics before this course.

Research Module 8

Introduction to Neuroimaging Methods

Dr Olaf Hauk, MRC Cognition and Brain Sciences Unit

More information:

Lectures and workshops will take place in the MRC Cognition and Brain Sciences Unit. They are structured in three blocks: MRI, fMRI and Connectivity, as well as EEG/MEG and Multimodal Imaging. Workshops usually take about 2 hours, lectures may be shorter. Attendees of the neuroimaging workshops are expected to have basic knowledge of scientific programming (e.g. Matlab, R, Python). Laptops will be provided for the workshops.

Research Module 9

Computational Neuroscience

Professor Máté Lengyel, Dr Guillaume Hennequin, Dr Timothy O’Leary, Engineering

Course info and content can be found here:

Please note: The course is 16 lectures and you do not have to attend every single lecture. The course covers basic topics in computational neuroscience and demonstrates how mathematical analysis and ideas from dynamical systems, machine learning, optimal control, and probabilistic inference can be applied to gain insight into the workings of biological nervous systems. The course also highlights a number of real-world computational problems that need to be tackled by any ‘intelligent’ system, as well as the solutions that biology offers to some of these problems. You do not have to complete the coursework for this module.