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Department of Applied Mathematics and Theoretical Physics

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

DeepClean: A generative deep neural network for self-supervised artefact detection in physiological signals
T Edinburgh, M Czosnyka, P Smielewski, M Cabeleira, S Eglen, A Ercole
– Acta Neurochirurgica: Supplementum
(2021)
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 Comput Biol
(2020)
16,
e1008316
From random to regular: Variation in the patterning of retinal mosaics*
PW Keeley, SJ Eglen, BE Reese
– The Journal of comparative neurology
(2020)
528,
2135
Open Code and Peer Review
S Eglen, E Lieungh
– Open Science Talk
(2020)
Functional characterization of human pluripotent stem-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
(2019)
9,
17125
CODECHECK: An open-science initiative to facilitate sharing of computer programs and results presented in scientific publications
S Eglen, D Nüst
– Septentrio Conference Series
(2019)
DeepClean - self-supervised artefact rejection for intensive care waveform data using generative deep learning.
T Edinburgh, P Smielewski, M Czosnyka, SJ Eglen, A Ercole
– CoRR
(2019)
abs/1908.03129,
Burst Detection Methods.
E Cotterill, SJ Eglen
– No journal
(2019)
22,
185
Recent developments in scholarly publishing to improve research practices in the life sciences
SJ Eglen, R Mounce, L Gatto, AM Currie, Y Nobis
– Emerging Topics in Life Sciences
(2018)
2,
775
meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays
S Gelfman, Q Wang, Y-F Lu, D Hall, CD Bostick, R Dhindsa, M Halvorsen, KM McSweeney, E Cotterill, T Edinburgh, MA Beaumont, WN Frankel, S Petrovski, AS Allen, MJ Boland, DB Goldstein, SJ Eglen
– PLoS Comput. Biol.
(2018)
14,
e1006506
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Research Group

Computational Biology