Dr Michael McCullough

Jubilee Joint Fellow, School of Computing & Eccles Institute, John Curtin School of Medical Research

Dr McCullough is a computational neuroscientist who works at the interface between complex biological phenomena and methods from machine learning and mathematics to study how the brain processes information and controls behaviour. He was awarded his BEng (Hons) in electrical and electronic engineering and BMus (Hons) in music performance in 2013, and his PhD in applied mathematics in 2018 from The University of Western Australia. After graduating he was a postdoctoral research associate with the UWA Young Lives Matter Foundation working on new computational approaches in clinical mental health. In late 2018 he moved to the Queensland Brain Institute at The University of Queensland as a research fellow in computational neuroscience studying information processing in the developing brain using a range of approaches including computer vision, network science, data science, complex systems and computational ethology. At the end of 2022 Dr McCullough joined ANU as a Jubilee Joint Fellow with the School of Computing and the Eccles Institute at the John Curtin School of Medical Research.

Research interests

The field of neuroscience is rapidly evolving as emerging technologies enable us to record activity from thousands of neurons in awake and behaving animals. However, analysing the massive amounts of data generated by these experiments is fast becoming a limiting factor, from both technical and conceptual perspectives. Dr McCullough’s research focuses on the development and application of new computational methods to identify biologically interpretable patterns and relationships in large-scale recordings of neural activity and behaviour. He works closely with leading experimental researchers at the Eccles Institute of Neuroscience and international collaborators to uncover new insights into neural coding and information processing in the brain.

  • McCullough, M & Goodhill, G 2021, 'Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain', Current Opinion in Neurobiology, vol. 70, pp. 89-100.
  • Avitan, L, Pujic, Z, Mölter, J et al. 2020, 'Behavioral Signatures of a Developing Neural Code', Current Biology, vol. 30, no. 17, pp. 3352-3363.e5.
  • McCullough, M, Small, M, Iu, H et al. 2017, 'Multiscale ordinal network analysis of human cardiac dynamics', Philosophical Transactions of the Royal Society Series A, vol. 375, no. 2096.
  • McCullough, M, Sakellariou, K, Stemler, T et al. 2017, 'Regenerating time series from ordinal networks', Chaos An Interdisciplinary Journal of Nonlinear Science, vol. 27, no. 3.
  • Sakellariou, K, McCullough, M, Stemler, T et al. 2016, 'Counting forbidden patterns in irregularly sampled time series. ii. reliability in the presence of highly irregular sampling', Chaos An Interdisciplinary Journal of Nonlinear Science, vol. 26, no. 12.
  • McCullough, M, Sakellariou, K, Stemler, T et al. 2016, 'Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitter', Chaos An Interdisciplinary Journal of Nonlinear Science, vol. 26, no. 12.
  • McCullough, M, Small, M, Stemler, T et al. 2015, 'Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems', Chaos An Interdisciplinary Journal of Nonlinear Science, vol. 25, no. 5.

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