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Title: Modelling and analysing of neural network dynamics
Author: Pernelle, Guillaume
ISNI:       0000 0004 7969 8308
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Neurons are often considered as the basic functional units of the brain. Our brain contains about 100 billions of neurons, of many different types and connected among themselves in a multitude of different ways. Yet, from this overwhelming complexity, order can emerge in the form of neural dynamics, when cohorts of neurons show synchronous activity. For example, oscillations of neural activity can be observed in many brain regions and are thought to be associated with cognitive processes such as information transfer or memory consolidation. However, too much oscillation can be detrimental, as during epilepsy crisis. Therefore, there must be mechanisms regulating the dynamics of neural networks. Our aim in this thesis is to further our understanding of neural networks dynamics. First, we modelled a network of cortical neurons known to exhibit oscillations. In this network, GABAergic interneurons are connected via chemical and electrical synapses (also called gap junctions), which promote synchronisation of neural assemblies. We implemented a novel model of gap junction plasticity, based on recent experimental evidence that they alter their strength in an activity-dependent manner. We hypothesised that gap junction plasticity can regulate network-wide neural dynamics and we investigated functional implications on information transmission in cortex. We then considered a brain region rich in neuronal gap junctions, the thalamic reticular nucleus (TRN). The TRN is thought to be the source of patterns of waxing-and-waning oscillations called spindles. We hypothesised that gap junction plasticity could lead to spindles and we simulated their pharmacological manipulation. Finally, we analysed and modelled the relationship between neural activity and hemodynamic response from in vivo recordings. We hypothesised that this relationship can be modelled with a transfer function. We then investigated the transfer function dependency of location and brain states. At last, we studied the prediction of neural activity from hemodynamic response.
Supervisor: Clopath, Claudia Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral