| Principles of computational modelling in neuroscience D Sterratt, B Graham, A Gillies, G Einevoll, D Willshaw Cambridge University Press, 2023 | 503 | 2023 |
| Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows G Papamakarios, DC Sterratt, I Murray The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 400 | 2019 |
| geometry: Mesh generation and surface tesselation CB Barber, K Habel, R Grasman, RB Gramacy, A Stahel, DC Sterratt R Package Version 0.3-1, URL http://cran. r-project. org/web/packages …, 2012 | 125* | 2012 |
| Standard anatomical and visual space for the mouse retina: computational reconstruction and transformation of flattened retinae with the Retistruct package DC Sterratt, D Lyngholm, DJ Willshaw, ID Thompson PLoS Computational Biology 9 (2), e1002921, 2013 | 111 | 2013 |
| geometry: Mesh Generation and Surface Tesselation K Habel, R Grasman, RB Gramacy, A Stahel, DC Sterratt R package version 0.3-6, 2015 | 68* | 2015 |
| A unified resource and configurable model of the synapse proteome and its role in disease O Sorokina, C Mclean, MDR Croning, KF Heil, E Wysocka, X He, ... Scientific reports 11 (1), 9967, 2021 | 36 | 2021 |
| Q10: The Effect of Temperature on Ion Channel Kinetics DC Sterratt Encyclopedia of Computational Neuroscience, 2551-2552, 2015 | 34 | 2015 |
| The 22nd International Conference on Artificial Intelligence and Statistics G Papamakarios, D Sterratt, I Murray PMLR, 2019 | 25 | 2019 |
| Quantitative assessment of computational models for retinotopic map formation JJ Hjorth, DC Sterratt, CS Cutts, DJ Willshaw, SJ Eglen Developmental Neurobiology 75 (6), 641-666, 2015 | 21 | 2015 |
| Spine calcium transients induced by synaptically-evoked action potentials can predict synapse location and establish synaptic democracy DC Sterratt, MR Groen, RM Meredith, A Van Ooyen PLoS Comput Biol 8 (6), e1002545, 2012 | 21 | 2012 |
| geometry: Mesh generation and surface tessellation JR Roussel, CB Barber, K Habel, R Grasman, RB Gramacy, ... R package version 0.4 6, 2022 | 17 | 2022 |
| Optimal learning rules for familiarity detection A Greve, DC Sterratt, DI Donaldson, DJ Willshaw, MCW Van Rossum Biological Cybernetics 100 (1), 11-19, 2009 | 14 | 2009 |
| Inhomogeneities in heteroassociative memories with linear learning rules DC Sterratt, D Willshaw Neural Computation 20 (2), 311-344, 2008 | 14 | 2008 |
| Analysis of local and global topographic order in mouse retinocollicular maps DJ Willshaw, DC Sterratt, A Teriakidis Journal of Neuroscience 34 (5), 1791-1805, 2014 | 9 | 2014 |
| Does morphology influence temporal plasticity? DC Sterratt, A van Ooyen International Conference on Artificial Neural Networks, 186-191, 2002 | 9 | 2002 |
| Locust Olfaction DC Sterratt Emergent neural computational architectures based on neuroscience, 270-284, 2001 | 8 | 2001 |
| Goldman-Hodgkin-Katz Equations DC Sterratt Encyclopedia of Computational Neuroscience, 1300-1302, 2015 | 7* | 2015 |
| Spikes, synchrony, sequences and Schistocerca's sense of smell DC Sterratt The University of Edinburgh, 2002 | 7 | 2002 |
| Is a biological temporal learning rule compatible with learning Synfire chains? DC Sterratt Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference …, 1999 | 7 | 1999 |
| Nernst Equation DC Sterratt Encyclopedia of Computational Neuroscience, 1843-1844, 2015 | 6* | 2015 |
Oksana SorokinaResearch Associate, University of EdinburghEmail verificata su ed.ac.uk
