PhD student, Institute of Cognitive Neuroscience
Alexandra House, 17 Queen Square, UCL
London WC1N 3AR
Doctoral training centre CoMPLEX:
Centre for Mathematics and Physics in the Life Sciences and Experimental Biology
ucl.ac.uk/complex | UCL Physics Building, Gower Street | +44 (0)20 7679 4325 | email@example.com
Life before UCL. I was born and raised in London, and graduated from Warwick University in 2009 with a BSc in Mathematics and Physics. I then spent a year as an English teacher in Moscow, where I realised that I wanted to do a PhD in Neuroscience.
At UCL. In 2010-11 I worked as a postgraduate researcher in Neil Burgess's lab at UCL's Institute of Cognitive Neuroscience. The lab investigates spatial representation in the brain, primarily using extracellular recording of rat hippocampus and entorhinal cortex. Much of the work I did was data analysis, but I did also undertake a small amount of experimental work. Neil's postdoc Caswell Barry provided day-to-day supervision.
In October 2011 I joined UCL's CoMPLEX doctoral training centre on its MRes + PhD program. In the first few weeks of the program I received a formal introduction to various aspects of cutting edge biology. I then undertook a series of three six-week projects followed by a longer project during the summer. In October 2012 I returned to Neil Burgess's lab, now as a PhD student co-supervised by John O'Keefe.
Theta phase precession of grid and place cell firing in 2d (Poster)
SfN, Oct 2012 | SfN page | A. Jeewajee, C. Barry, C. Lever, V. Douchamps, D. Manson, N. Burgess
Theta-phase precession, a remarkably robust example of temporal coding in the brain, is found in two types of spatially tuned neurons in the hippocampal formation. It has been explored in some detail in place cells, and to a lesser degree in grid cells, with most analyses concentrating on firing on linear tracks. Here we characterize the theta-phase of firing of grid cells and place cells in rats foraging in open field environments. Firing phase relative to the theta rhythm of the local field potential is described as a function of the animal's location within the current firing field relative to the running direction during reasonably straight runs, extending the idea of "directional rate zones". Consistent with data from linear tracks, phase precession is most reliable in non-directional grid cells from superficial layers, while directional (or conjunctive) cells exhibit no or extremely weak phase precession. Place cells and grid cells with phase precession show firing phases that transition from "late" phase (and low phase variance) in the first half of the field, to an earlier phase (and high phase variance) after the animal moves through the field by a distance equivalent to the field radius. This relatively sharp transition is obscured when combining runs into "linearized" plots that ignore the lateral displacement of the run from the field centre.
Contrast Invariance of Cell Populations in V1
Jan 2012 | pdf | supervised by Matteo Carandini
Cells in mammalian primary visual cortex respond preferentially to bars of lightness or darkness on the retina. For a long time it has been known that the extent of this sensitivity does not depend on the contrast of the stimulus. It has recently been shown that this invariance also exists in the shape of the time-averaged population response. Here we examine whether invariance of the population holds over much shorter time periods, and tentatively conclude that it does.
Modeling the Metabolic Interactions of Astrocytes and Neurons
Under Normal Conditions and During Ischemic Hypoxia
Mar 2012 | pdf | supervised by Ilias Tachtsidis
It is relatively common for the brain of perinatal infants to be exposed to a period of reduced oxygen (hypoxia) and reduced blood flow (ischemia). Left untreated, these episodes tend to cause permanent damage to the brain. In order to understand how this damage arises and whether it is preventable, an extensive computational model has been developed that attempts to accurately capture the full metabolic complexity of the infant brain. This complexity encompasses everything from haemodynamics, to calcium channels in vascular muscle, to oxidative phosphorylation in the mitochondria of neurons. Model parameters are set using data from in vivo and in vitro experiments, as well as using a range of more ad hoc measures such as steady-state analysis. In the current model there is no distinction between astrocytes and neurons, i.e. the cell in the model is a rough average of the two. However there is plenty of evidence that astrocytes and neurons have quite different metabolic properties and interact in non-trivial ways. In this work we examine a separate model, designed for looking at the metabolic interactions of astrocytes and neurons, and examine whether any of its dynamics should be incorporated into the hypoxia model.
Endoscopic Transplantation of Esophageal Mucosa Grown In Vitro
Using Magnetic Particles in an Externally Administered Magnetic Field
May 2012 | pdf | supervised by Richard Day and Quentin Pankhurst
If identified at an early stage, cancer of the esophagus can be removed non-invasively using an endoscope. However, the site of removed tissue rarely recovers completely, and is commonly replaced by a large volume of scar tissue, in some cases causing a significant narrowing of the esophagus. To prevent such post-operative problems it has been proposed that healthy replacement tissue be applied to the site of the cancer, having been grown in vitro from the patient's own cells. In this work we consider one method for improving the adhesion of the new cells to the target site. The method involves seeding the in vitro cells with magnetic particles, and subsequently applying a magnetic force to them using an externally administered permanent magnet, applied for a few minutes during the grafting procedure.
The Seas are Full of Krill (Poster)
Jun 2012 | pdf | transferable skills mini-project
Following a week-long course at the Marine Biology Assosiation's laboratory in Plymouth, MRes students are required to produce a poster relating to something they have learnt. Producing and presenting the poster gives students a chance to practice important 'transferable' skills. I chose to make a poster on krill and their role in the marine carbon system.
Characterizing Brain Activity at the Mesoscopic Scale
Comparing a Kuramoto model to Resting State MEG data
August 2012 | pdf | supervised by Luc Berthouze and Simon Farmer
Despite impressive advances over the last several decades, to a large extent the human brain remains something of a black-box. This state of affairs is easily understood given that the `machine inside the box' consists of a network of approximately 1011 nodes, each of which transmits electrical signals in a highly non-linear and ever changing manner. Investigation of individual cell types has been relatively successful in primary sensorary areas and hippocompus, but there is yet
to be any signiffcant progress in understanding the dynamics of small populations of cells. Appropriate grouping of these small populations results in a brain divided into approximately 1000 regions. These regions can then be further aggregated to produce a labeled map of the brain at the mesoscopic scale where there are about 66 distinct regions. EEG, MEG and fMRI make it possible to record brain activity at this coarse level of network description. In this work we model the 66 regions as a network of weakly coupled oscillators, i.e as a Kuramoto model, and compare model simulations to MEG data collected at resting state.
I am a keen programmer. Recently I have become a big fan of MATLAB, but I also dabble in C and various other bits and bobs.
Pretty much all aspects of neuroscience interest me: from the philosophical (Dennett's pretty good) to the biochemical, and everything in between. The algorithmic/cognitive/systems level of research I find most fascinating (you can't beat Marr).
I'd love to help build the next generation of instrumentation for doing cognitive neuroscience. Nano-neuro-engineering may one day soon allow us to record from thousands or even millions of neurons simultaneously. Only then will we be able to run the brain 'in debug mode' and really find out what is going on. (I'm also hopeful that connectomics has a good future on this front.)