• Sterratt, D. C., Sorokina, O. and Armstrong, J. D. (2015). ‘Integration of rule-based models and compartmental models of neurons’. In O. Maler, Á. Halász, T. Dang and C. Piazza, eds., Hybrid Systems Biology: Second International Workshop, HSB 2013, Taormina, Italy, September 2, 2013 and Third International Workshop, HSB 2014, Vienna, Austria, July 23-24, 2014, Revised Selected Papers, vol. 7699 of Lecture Notes in Bioinformatics, pp. 143–158. Springer International Publishing, Cham. doi: 10.1007/978-3-319-27656-4_9. Preprint at arXiv:1411.4980

  • Sterratt, D. C. (2015). Invited articles on ‘Nernst Equation’, ‘Goldman-Hodgkin-Katz Equations’, ‘Gating Current’, ‘Nomenclature of Ion Channels (IUPHAR Scheme)’ and ‘Q10: the Effect of Temperature on Ion Channel Kinetics’. In Jaeger, D. and Jung, R., eds. (2015). Encyclopedia of Computational Neuroscience. Springer New York

  • Hjorth, J. J. J., Savier, E., Sterratt, D. C., Reber, M. and Eglen, S. J. (2015). ‘Estimating the location and size of retinal injections from orthogonal images of an intact retina’. BMC Neuroscience 16:80 PDF

  • Hjorth, J. J. J., Sterratt, D. C., Cutts, C. S., Willshaw, D. J. and Eglen, S. J. (2015). ‘Quantitative assessment of computational models for retinotopic map formation’ Developmental Neurobiology 75: 641–666. doi:10.1002/dneu.22241. Preprint available at arXiv:1408.6132

  • Willshaw, D. J., Sterratt, D. C. and Teriakidis, A. (2014). ‘Analysis of local and global topographic order in mouse retinocollicular maps’. Journal of Neuroscience 34:1791-1805. doi:10.1523/JNEUROSCI.5602-12.2014 [PDF]

  • Sterratt, D. C. and Hjorth, J. J. J. (2013). ‘Retinocollicular mapping explained?’ Visual Neuroscience30:125-128. doi:10.1017/S0952523813000254 [PDF]

  • Sterratt, D. C. (2013). ‘On the Importance of Countergradients for the Development of Retinotopy: Insights from a Generalised Gierer Model’. PLoS ONE 8:e67096. doi:10.1371/journal.pone.0067096 [PDF]

  • Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013). ’Standard anatomical and visual space for the mouse retina: Computational reconstruction and transformation of flattened retinae with the Retistruct Package’. PLoS Computational Biology 9(2): e1002921. doi:10.1371/journal.pcbi.1002921

  • Sterratt, D. C., Groen, M. R., Meredith, R. M. and van Ooyen, A. (2012). ‘Spine calcium transients induced by synaptically-evoked action potentials can predict synapse location and establish synaptic democracy’. PLoS Computational Biology 8(6): e1002545. doi:10.1371/journal.pcbi.1002545 [Code on ModelDB]

  • Sterratt, D., Graham, B., Gillies, A. and Willshaw, D. (2011). Principles of Computational Modelling in Neuroscience. Cambridge University Press

  • Greve, A., Sterratt, D. C., Donaldson, D. I., Willshaw, D. J. and Rossum, M. C. W. (2009) ‘Optimal learning rules for familiarity detection’ Biological Cybernetics 100:11-19. doi:10.1007/s00422-008-0275-4

  • Sterratt, D. C. and Willshaw, D. (2008). ‘Inhomogeneities in heteroassociative memories with linear learning rules’ Neural Computation 20:311-344. [Preprint]

  • Sterratt, D. C., Auzinger, W. et al. (2005). ‘An introduction to MATLAB for neuroscience research’. Electronic document released under the GNU Free Documentation License. [PDF] [Source, TGZ]

  • Sterratt, D. C. and van Ooyen, A. (2004). ‘Does a dendritic democracy need a ruler?’ Neurocomputing 58-60:437-442. doi:10.1016/j.neucom.2004.01.078. [PDF]

  • Gillies, A. J. and Sterratt, D. C. (2003). ‘Neuron tutorial’. Web document. http://www.anc.ed.ac.uk/school/neuron/

  • Sterratt, D. C. and van Ooyen, A. (2002). ‘Does morphology influence temporal plasticity?’ In J. R. Dorronsoro, ed., Artificial Neural Networks – ICANN 2002, vol. 2415 of Lecture Notes in Computer Science, pp. 186-191. Springer-Verlag, Berlin, Heidelberg, New York. [PDF]

  • Sterratt, D. C. (2001b). Spikes, synchrony, sequences and Schistocerca’s sense of smell. Ph.D. thesis, University of Edinburgh. [PDF]

  • Sterratt, D. C. (2001a). ‘Locust olfaction: Synchronous oscillations in excitatory and inhibitory groups of spiking neurons’. In S. Wermter, J. Austin and D. Willshaw, eds., Emergent Neural Computational Architectures Based on Neuroscience, vol. 2036 of Lecture Notes in Artificial Intelligence, pp. 270-284. Springer-Verlag, Berlin Heidelberg. [PDF]

  • Sterratt, D. C. (1999). ‘Is a biological temporal learning rule compatible with learning synfire chains?’ In ICANN99: Ninth International Conference on Artificial Neural Networks, pp. 551-556. Institute of Electrical Engineers, London. [PDF]