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Department of Applied Mathematics and Theoretical Physics


  • 1997-2000 Wellcome Trust Fellow in Mathematical Biology, Edinburgh
  • 2000-2001 Lecturer, School of Informatics, Edinburgh
  • 2001-2004 Wellcome Trust Travelling Fellowship, St Louis and Edinburgh
  • 2004-2006 Lecturer, DAMTP
  • 2006-2015 Senior Lecturer, DAMTP
  • 2015- Reader. DAMTP


Stephen Eglen is a computational neuroscientist: he uses computational methods to study the development of the nervous system, using mostly the retina and other parts of the visual pathway as a model system. He is particularly interested in questions of structural and functional development:

Structural development: how do retinal neurons acquire their positional information within a circuit?

Functional development: what are the mechanisms by which neurons make contact with each other, to perform functioning circuits?

Selected Publications

Please see my publications page


CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility
D Nüst, SJ Eglen
– F1000Research
Ten simple rules for writing Dockerfiles for reproducible data science.
D Nüst, V Sochat, B Marwick, SJ Eglen, T Head, T Hirst, BD Evans
– PLOS Computational Biology
Ten Simple Rules for Writing Dockerfiles for Reproducible Data Science
D Nüst, V Sochat, B Marwick, S Eglen, T Head, T Hirst, B Evans
From random to regular: Variation in the patterning of retinal mosaics.
PW Keeley, SJ Eglen, BE Reese
– J Comp Neurol
Open Code and Peer Review
S Eglen, E Lieungh
– Open Science Talk
Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures.
T Hyvärinen, A Hyysalo, FE Kapucu, L Aarnos, A Vinogradov, SJ Eglen, L Ylä-Outinen, S Narkilahti
– Sci Rep
CODECHECK: An open-science initiative to facilitate sharing of computer programs and results presented in scientific publications
S Eglen, D Nüst
– Septentrio Conference Series
DeepClean: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning
T Edinburgh, P Smielewski, M Czosnyka, M Cabeleira, SJ Eglen, A Ercole
Burst detection methods
E Cotterill, SJ Eglen
Recent developments in scholarly publishing to improve research practices in the life sciences.
SJ Eglen, R Mounce, L Gatto, AM Currie, Y Nobis
– Emerg Top Life Sci
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Research Group

Computational Biology