Doctor Travis Monk

Doctor Travis Monk

Postdoctoral Research Fellow,
International Centre for Neuromorphic Systems


My primary research interest is in developing a quantitative theory for how nervous systems represent the world and make decisions.

That goal requires expertise in quantitative methods and biological sciences, particularly neuroscience.

My experience in quantitative methods include a BSc in Physics from Truman State University (honours), an MSc in Computational Neuroscience from the University of Plymouth (high distinction), and several first-author publications in statistics, probability theory, and stochastic processes.

My experience in biological science include a PhD in Zoology from the University of Otago (honours), and a couple first-author publications in the evolutionary history of nervous systems.

I am reasonably proficient in both object-oriented and agent-based computer programming, with experience in Python and Netlogo.  

I am available to supervise Masters and PhD students, particularly those with expertise in statistics, changepoint detection, likelihood ratio tests, and computer programming.  I apply these techniques to computational neuroscience and show how neurons can implement them in real-time.  Please contact me to arrange a meeting if you are interested, or know someone who might be.

This information has been contributed by Doctor Monk.


  • Changepoint Detection
  • Computational Neuroscience
  • Early Animal Evolution
  • Martingales
  • Phylogenetic Relationships
  • Probability Theory
  • Stochastic Processes

Organisational Unit (School / Division)

  • International Centre for Neuromorphic Systems


Location: Penrith (Werrington South)

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Journal Articles

  • Islam, M., Xu, Y., Monk, T., Afshar, S. and Schaik, A. (2022), 'Noise-robust text-dependent speaker identification using cochlear models', Journal of the Acoustical Society of America, vol 151, no 1 , pp 500 - 516.
  • Monk, T. and Schaik, A. (2022), 'Martingales and the fixation time of evolutionary graphs with arbitrary dimensionality', Royal Society Open Science, vol 9, no 5 .
  • Monk, T. and Schaik, A. (2021), 'Martingales and the characteristic functions of absorption time on bipartite graphs', Royal Society Open Science, vol 8, no 10 .
  • Monk, T. and Schaik, A. (2020), 'Wald's martingale and the conditional distributions of absorption time in the Moran process', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol 476, no 2241 .
  • Monk, T., Savin, C. and Lucke, J. (2018), 'Optimal neural inference of stimulus intensities', Scientific Reports, vol 8 .
  • Monk, T. (2018), 'Martingales and the fixation probability of high-dimensional evolutionary graphs', Journal of Theoretical Biology, vol 451 , pp 10 - 18.
  • Monk, T., Savin, C. and Lucke, J. (2016), 'Neurons equipped with intrinsic plasticity learn stimulus intensity statistics', Advances in Neural Information Processing Systems, vol 29 , pp 4285 - 4293.
  • Monk, T., Paulin, M. and Green, P. (2015), 'Ecological constraints on the origin of neurones', Journal of Mathematical Biology, vol 71, no 6-7 , pp 1299 - 1324.
  • Monk, T., Green, P. and Paulin, M. (2014), 'Martingales and fixation probabilities of evolutionary graphs', Proceedings of the Royal Society A, vol 470, no 2165 .
  • Monk, T. and Paulin, M. (2014), 'Predation and the origin of neurones', Brain, Behavior and Evolution, vol 84, no 4 , pp 246 - 261.
  • Monk, T. and Paulin, M. (2013), 'Bayesian inference from single spikes', BMC Neuroscience, vol 14, no Suppl. 1 .
  • Furlani, E., Sahoo, Y., Ng, K., Wortman, J. and Monk, T. (2007), 'A model for predicting magnetic particle capture in a microfluidic bioseparator', Biomedical Microdevices, vol 9, no 4 , pp 451 - 463.

Conference Papers

  • Islam, M., Xu, Y., Afshar, S., Monk, T. and Schaik, A. (2021), 'Investigation of auditory nerve model and conventional approaches in noise-robust speaker identification', International Conference on Frontiers of Signal Processing, online.
  • Monk, T. and Harris, C. (2009), 'Using optimality to predict photoreceptor distribution in the retina', ICONIP (Conference), Auckland, N.Z..

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