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Publications

  • 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 In press
  • 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. [PDF]
  • 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. http://dx.doi.org/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]