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Epileptic seizures are characterised by abnormal brain dynamics at multiplescales, engaging single neurons, neuronal ensembles and coarse brain regions.Key to understanding the cause of such emergent population dynamics, iscapturing the collective behaviour of neuronal activity at multiple brainscales. In this thesis I make use of the larval zebrafish to capture singlecell neuronal activity across the whole brain during epileptic seizures.Firstly, I make use of statistical physics methods to quantify the collectivebehaviour of single neuron dynamics during epileptic seizures. Here, Idemonstrate a population mechanism through which single neuron dynamicsorganise into seizures: brain dynamics deviate from a phase transition.Secondly, I make use of single neuron network models to identify the synapticmechanisms that actually cause this shift to occur. Here, I show that thedensity of neuronal connections in the network is key for driving generalisedseizure dynamics. Interestingly, such changes also disrupt network responseproperties and flexible dynamics in brain networks, thus linking microscaleneuronal changes with emergent brain dysfunction during seizures. Thirdly, Imake use of non-linear causal inference methods to study the nature of theunderlying neuronal interactions that enable seizures to occur. Here I showthat seizures are driven by high synchrony but also by highly non-linearinteractions between neurons. Interestingly, these non-linear signatures arefiltered out at the macroscale, and therefore may represent a neuronalsignature that could be used for microscale interventional strategies. Thisthesis demonstrates the utility of studying multi-scale dynamics in the larvalzebrafish, to link neuronal activity at the microscale with emergent propertiesduring seizures.

Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution