Hugh Osborne

Hugh Osborne


I am a computational neuroscientist with a strong interest in building and analysing dynamical systems for modelling neuronal behaviour with particular emphasis on bursting and oscillations for central pattern generators. 

PhD in The School of Computing : Population Density Techniques for Spinal Cord Models

Supervisor : Dr Marc de Kamps

Research interests

A Reduction of the Rybak Bursting Neuron to Two Dimensions for use in Half-center Oscillators   
In preparation
The four dimensional bursting neuron model used in the Rybak CPG [1] is minimal for a Hodgkin Huxley style neuron. However, I have reduced the model further using similar techniques to the derivation of the Izhikevich simple model in order to better understand its escape/release mechanism when placed in a half centre circuit; to identify a “working range” of noise or variability required to maintain oscillations; and to enable faster prototyping of population models to support the results of dynamic and cyclical task experiments. This reduction exercise has demonstrated that, for this model at least, neither half-center reaches a steady state before the escape/release switch occurs and therefore other parameters such as the bursting period or the amount of noise become important for maintainable oscillations.

A Flexible Population Density Technique for Three Dimensional Neuron Models and Beyond
In preparation
MIIND is a neural simulator which uses a geometric population density technique to describe the statistical behaviour of many interacting populations. Until recently, the technique has been limited to simulating populations of neurons with models of up of two time dependent variables due in part to a non-trivial discretisation step required from the user before simulations can be run. I have introduced a way to automate the required preprocessing steps which allows the simulation technique to be generalised to higher dimensional models, increase the available types of coupling between populations such as those which produce voltage or time dependent post-synaptic potentials, and significantly speed up the workflow to make model prototyping more efficient. Density plots produced by this software can help us understand the behaviour of populations of neurons, particularly in cases where the activity occurs below or is pushed just above threshold. Finally, I have integrated the MIIND software package into The Virtual Brain [2] enabling the simulation of a full brain model which spans the scales from individual neuron behaviour to simulated EEG recordings.

Recent Publications

Muscles Recruited During an Isometric Knee Extension is Defined by Proprioceptive Feedback
York, G., Osborne, H., Sriya, P., Astill, S., De Kamps, M. and Chakrabarty, S., 2019. BioRxiv, p.802736.
My contribution to this article consisted of building a spinal circuit population model to capture large scale behaviours of motor neuron populations governing five muscles in the human leg during an isometric knee extension task. I used a geometric population density technique developed at the University of Leeds to simulate the behaviour of the interacting populations of motor neurons and spinal interneurons then performed Non-negative Matrix Factorisation on the resulting average firing rates. These results were then compared with the results of NMF performed on EMG recordings from a leg task experiment to support evidence that proprioception is implicated in such tasks. I was also able to position this population model in the context of larger central pattern generator models developed by Ilya Rybak and his team which forms the basis of my further research mentioned below.


[1] McCrea, D.A. and Rybak, I.A., 2008. Organization of mammalian locomotor rhythm and pattern generation. Brain research reviews, 57(1), pp.134-146.

[2] Sanz Leon, P., Knock, S.A., Woodman, M.M., Domide, L., Mersmann, J., McIntosh, A.R. and Jirsa, V., 2013. The Virtual Brain: a simulator of primate brain network dynamics. Frontiers in neuroinformatics, 7, p.10.


  • MEng in Computing (Imperial College)
  • MSci in Computing (University of Leeds)