  Epileptic seizures are characterised by abnormal brain dynamics at multiple
scales, engaging single neurons, neuronal ensembles and coarse brain regions.
Key to understanding the cause of such emergent population dynamics, is
capturing the collective behaviour of neuronal activity at multiple brain
scales. In this thesis I make use of the larval zebrafish to capture single
cell neuronal activity across the whole brain during epileptic seizures.
Firstly, I make use of statistical physics methods to quantify the collective
behaviour of single neuron dynamics during epileptic seizures. Here, I
demonstrate a population mechanism through which single neuron dynamics
organise into seizures: brain dynamics deviate from a phase transition.
Secondly, I make use of single neuron network models to identify the synaptic
mechanisms that actually cause this shift to occur. Here, I show that the
density of neuronal connections in the network is key for driving generalised
seizure dynamics. Interestingly, such changes also disrupt network response
properties and flexible dynamics in brain networks, thus linking microscale
neuronal changes with emergent brain dysfunction during seizures. Thirdly, I
make use of non-linear causal inference methods to study the nature of the
underlying neuronal interactions that enable seizures to occur. Here I show
that seizures are driven by high synchrony but also by highly non-linear
interactions between neurons. Interestingly, these non-linear signatures are
filtered out at the macroscale, and therefore may represent a neuronal
signature that could be used for microscale interventional strategies. This
thesis demonstrates the utility of studying multi-scale dynamics in the larval
zebrafish, to link neuronal activity at the microscale with emergent properties
during seizures.
