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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


Rights and Retention Strategy: a Primer from UKRN
R Network, S Eglen
Bayesian model selection for multilevel models using integrated likelihoods
T Edinburgh, A Ercole, S Eglen
– PLoS One
Bayesian model selection for multilevel models using integrated likelihoods
T Edinburgh, A Ercole, SJ Eglen
Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation.
T Edinburgh, SJ Eglen, P Thoral, P Elbers, A Ercole
– Gigabyte
Analysis of Activity Dependent Development of Topographic Maps in Neural Field Theory with Short Time Scale Dependent Plasticity
N Gale, J Rodger, M Small, S Eglen
– Mathematical Neuroscience and Applications
Volume 2,
Homophilic wiring principles underpin neuronal network topologyin vitro
D Akarca, A Dunn, P Hornauer, S Ronchi, M Fiscella, C Wang, M Terrigno, R Jagasia, P Vértes, S Mierau, O Paulsen, S Eglen, A Hierlemann, D Astle, M Schröter
Causality indices for bivariate time series data: a comparative review of performance
T Edinburgh, SJ Eglen, A Ercole
– Chaos: an interdisciplinary journal of nonlinear science
Causality indices for bivariate time series data: A comparative review of performance.
T Edinburgh, SJ Eglen, A Ercole
– Chaos
DeepClean: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning
T Edinburgh, M Czosnyka, P Smielewski, M Cabeleira, S Eglen, A Ercole
– Acta Neurochirurgica: Supplementum
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
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Research Group

Computational Biology