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

Digital Twins of Human Brain: Where Artificial Intelligence (AI) and Neuroscience Meet

Dr Sam Nallaperuma-Herzberg, Computer Science and Technology (Michaelmas and Lent terms)

This short course serves as an introduction to spark interest among the next generation of interdisciplinary researchers in neuroscience and AI. It aims to inspire participants to pursue careers in this truly interdisciplinary field, with potential contributions to both healthcare and artificial intelligence (AI). Digital twins in healthcare is an emerging area with the potential to revolutionize the industry by enabling faster diagnoses and personalized treatment recommendations. In parallel, digital twin models of the human brain reveal key aspects of neural design that can inspire the development of more energy-efficient AI algorithms, reducing AI’s carbon footprint. The course is structured into three parts: Part 1 introduces the foundational concepts of digital twinning, followed by a case study on a digital twin model development in healthcare. Part 2 explores the neural design principles that make the human brain exceptionally energy-efficient, with references to information theory. It is followed by a case study on the development of brain-inspired, energy-efficient AI algorithms. Part 3 delves into more advanced digital models incorporating multiple modalities through multimodal AI techniques. This section also covers current research trends and includes a case study extending the digital twin model from Part 1 to handle multiple modalities. Whether participants come from a background in neuroscience, life sciences, or computational sciences, this course provides opportunities for them to apply their expertise, develop new skills, and grow within this interdisciplinary field.

Research Module 2

Loose Patch Clamp Recording

Dr Hugh Matthews & Professor Chris Huang, Department of Physiology, Development & Neuroscience

In this intensive workshop you will investigate the voltage and time dependent sodium and potassium conductances of mouse gastrocnemius muscle. The workshop will begin with a lecture comparing loose-seal and tight-seal patch clamp recording, and then look in depth at the implementation of the loose patch technique. You will then participate in recording the ionic currents flowing across the muscle surface membrane under voltage clamp using the loose patch clamp technique. From these records you will be able to analyse the voltage-dependent gating of the sodium and potassium conductances underlying the action potential. You will also have the opportunity to use a computer model incorporating the Hodgkin-Huxley equations to simulate some of the experiments which you will carry out, in order to improve your understanding of the way in which these conductances depend on membrane potential.

Research Module 3

Electron microscopy of the central nervous system

Dr Sebastian Timmler, Dr. Scott Dillon, Wellcome-MRC Cambridge Stem Cell Institute, JCBC

Electron microscopes were designed more than a century ago to overcome the diffraction limit of light. While the basic mechanism has not changed since then, modern microscopes are easy to use and reach nanoscopic resolution. This makes transmission electron microscopy (TEM) and scanning electron microscopy (SEM) essential tools for neuroscience to study the fine structure of the nervous system in 2D and 3D. In this course we will introduce you to the basic principles of TEM and SEM, demonstrate sample preparation and will image samples of the mouse optic nerve and cortex. We will use artificial intelligence-driven software to analyse 2D images and reconstruct 3D volumes of tissue. If you always wanted to look at synapses, axons, myelin sheaths and cell organelles in their tissue context, you have to take this course!

Research Module 4

Brain Machine Interface

Dr Yashar Ahmadian , Engineering

This course will provide a hands-on introduction to a key set of information engineering tools in the context of brain machine interfaces (BMI), an exciting and fast developing bioengineering technology. Following introductory lectures covering an overview of relevant neural circuits and recording and stimulation technology used in BMI, the bulk of the lectures will introduce various modelling, data analysis, and decoding techniques in the context of motor-oriented BMI and motor cortex.

Research Module 5

Critique of null hypothesis significance testing from the Bayesian perspective

Professor Paul Bays, Department of Psychology

Professor Bays 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: http://imaging.mrc-cbu.cam.ac.uk/methods/IntroductionNeuroimagingLectures

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: http://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-4g3-computational-neuroscience-2018-19

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.