Research interests
Rhythm and beat perception
Accurate time estimation is essential for survival, yet the neural basis for timing remain elusive. I am particularly interested in beat perception in music and rhythmic sound. Beat perception involves fast perception and learning of repetitive time intervals from 100 to 2000 ms. The ability to synchronise to an external rhythm appears to be inherent to humans. Strong evidence points towards a neural mechanism involving interactions between sensory and motor areas. We are developing a model of predictive timing using adaptive neuronal oscillator models.
Dynamical systems theory
Dynamical systems theory provides an invaluable tool for gaining insight into how things evolve in time. In particular, one can find the equilibrium conditions of the system and analyse its stability. Paired with bifurcation theory, dynamical systems theory allows us to explore how different parameters change the behaviour of the system. Employing these methods to model biological systems allows us to better understand their behaviour and suggest potential causes of discrepancies between healthy and diseased states.
Synchrony and brain rhythms
Rhythmic neural activity is readily observed in electrophysiological brain recordings. The transitions from high amplitude to low amplitude signals are thought to be caused by changes in the synchrony of the underlying neuronal population firing patterns. There are a plethora of models to describe neural dynamics at a range of different scales, yet very few which can describe synchronisation phenomena at an analytically tractably scale. In Byrne et al. (2017), we developed a parsimonious model for the dynamics of synchrony within a synaptically coupled spiking network, which has an exact mean field description. The reduced model allows us to explore the mechanisms linking population synchrony and scalp level activity patterns.
Neural field modelling
Neural field models have been actively used since the 1970s to model the coarse grained activity of large populations of neurons and synapses across space. They have proven especially useful in understanding brain rhythms and wave propagation in the brain. Although motivated by biology, these models are phenomenological in nature. They are built on the assumption that the neural tissue operates in a near synchronous regime, and hence, cannot account for changes in the underlying synchrony of patterns. I am particularly interested in developing a new class of neural activity models which can describe the evolution of within population synchrony at the mesoscopic scale, see Coombes & Byrne (2018).