I'm a theoretical neuroscience PhD student at the Institute of Cognitive Neuroscience, UCL. I'm funded by CoMPLEX, UCL's interdisciplinary centre for maths and physics in the life sciences.
To contact me, drop me an email: talfan.evans.13@ucl.ac.uk.
I have some things on GitHub.
For a summary of most of the contents of this website, see my CV.
Alternatively, get me on LinkedIn 
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OpenAFM is a collaborative project between UCL, MIT and Tsingua University to build a low-cost, open-source Atomic Force Microscope (AFM). Unlike optical microscopes, AFMs use lasers to measure the deflection of a physical cantilever, which is run across the surface of a sample. AFMs are capable of nano-scale resolution, and we're trying to build one for less than 100 pounds.
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SysMIC is a comprehensive online course in systems biology aimed at researchers in the biological sciences. It teaches a range of biological modelling techniques and statistical analysis methods, along with the fundamental mathematics useful for cross-disciplinary bioscience research.
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Simple@CoMPLEX is an outreach program developed by students at UCL's CoMPLEX research centre. The program aims to teach school children about using maths to model biological systems. To do this, we've developed a kit to help schools design a board game based around the ideas of evolutionary game theory. Trust us, it's a lot of fun!
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I wrote a short course on an introduction to engineering for 16-18 year olds applying to university. There are some useful notes on calculus, dynamics and mechanics as well as some fun building excercises / games that can be used in the classroom.
My work focusses on developing biologically realistic models of grid cell firing. In particular, I'm interested in how uncertainty and perceptual misinformation is represented on a network level and how this is ultimately manifested in behaviour - see this paper.
I'm also interested in the role of grid cells as a coding mechanism for space and more generally in unsupervised learning processes within the hippocampal formation.
I use a combination of spiking and rate-based neurons in network models to explore these ideas, taking inspiration from biologically realistic learning algorithms. Get my MatLab code for an attractor model of grid cell firing here.
I also apply machine learning to the processing and analysis of electrophysiological (see this paper), fMRI, behavioural and two-photon microscopy data from humans and rodents.
