
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: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning.
(2019)
(DOI: 10.1007/978-3-030-59436-7_45)
Recent developments in scholarly publishing to improve research practices in the life sciences.
– Emerg Top Life Sci
(2018)
2,
775
(DOI: 10.1042/ETLS20180172)
meaRtools: an R Package for the Analysis of Neuronal Networks Recorded on Microelectrode Arrays
– PLoS computational biology
(2018)
14,
e1006506
(DOI: 10.1371/journal.pcbi.1006506)
Scholarly Publishing, Freedom of Information and Academic Self-Determination: The UNAM-Elsevier Case
(2017)
Toward standard practices for sharing computer code and programs in neuroscience.
– Nature Neuroscience
(2017)
20,
770
(DOI: 10.1038/nn.4550)
A molecular mechanism for the topographic alignment of convergent neural maps.
– eLife
(2017)
6,
e20470
(DOI: 10.7554/elife.20470)
A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.
– Journal of neurophysiology
(2016)
116,
306
(DOI: 10.1152/jn.00093.2016)
Ten Simple Rules for Taking Advantage of Git and GitHub
– PLOS Computational Biology
(2016)
12,
e1004947
(DOI: 10.1371/journal.pcbi.1004947)
- <
- 2 of 11