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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

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation
T Edinburgh, SJ Eglen, P Thoral, P Elbers, A Ercole
– GigaByte (Hong Kong, China)
(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
S Eglen, M Small, J Rodger, N Gale
– Mathematical Neuroscience and Applications
(2022)
Volume 2,
8390
(doi: 10.46298/mna.8390)
Homophilic wiring principles underpin neuronal network topology in vitro
D Akarca, AWE Dunn, PJ Hornauer, S Ronchi, M Fiscella, C Wang, M Terrigno, R Jagasia, PE Vértes, SB Mierau, O Paulsen, SJ Eglen, A Hierlemann, DE Astle, M Schröter
(2022)
(doi: 10.1101/2022.03.09.483605)
Retinotopic Development, Models of
SJ Eglen
(2022)
3039
(doi: 10.1007/978-1-0716-1006-0_406)
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)
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.51738.2)
Causality indices for bivariate time series data: a comparative review of performance
T Edinburgh, SJ Eglen, A Ercole
(2021)
(doi: 10.48550/arxiv.2104.00718)
Causality indices for bivariate time series data: A comparative review of performance.
T Edinburgh, SJ Eglen, A Ercole
– Chaos
(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, S Eglen
(2021)
(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