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Department of Applied Mathematics and Theoretical Physics

Professor Stephen John Eglen

Career

  • 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

Research

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

Publications

Bayesian model selection for multilevel models using integrated likelihoods
T Edinburgh, A Ercole, S Eglen
– PloS one
(2023)
18,
e0280046
(DOI: 10.48550/arxiv.2207.02144)
Bayesian model selection for multilevel models using integrated likelihoods
T Edinburgh, A Ercole, SJ Eglen
(2022)
(DOI: 10.48550/arxiv.2207.02144)
Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation.
T Edinburgh, SJ Eglen, P Thoral, P Elbers, A Ercole
– Gigabyte
(2022)
2022,
gigabyte45
(DOI: 10.46471/gigabyte.45)
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
(2022)
Volume 2,
(DOI: 10.46298/mna.8390)
Homophilic wiring principles underpin neuronal network topology in 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
(2022)
(DOI: 10.1101/2022.03.09.483605)
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
(2021)
31,
083111
(DOI: 10.1063/5.0053519)
Causality indices for bivariate time series data: A comparative review of performance
T Edinburgh, SJ Eglen, A Ercole
– Chaos (Woodbury, N.Y.)
(2021)
31,
083111
(DOI: 10.1063/5.0053519)
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
(2021)
131,
235
(DOI: 10.1007/978-3-030-59436-7_45)
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
(2021)
10,
253
(DOI: 10.12688/f1000research.51738.1)
CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility
D Nüst, SJ Eglen
– F1000Res
(2021)
10,
253
(DOI: 10.12688/f1000research.51738.2)
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Research Group

Computational Biology

Email

S.J.Eglen@damtp.cam.ac.uk

Room

G0.11

Personal homepage

http://www.damtp.cam.ac.uk/user/sje30/

Study at Cambridge

About the University

Research at Cambridge