The brain is a prime example of a complex network. The nodes are comprised of neurons and linked through synapses, giving the network an anatomical structure. The fact that neurons communicate with each other through the firing of action potentials allows for the observation of network dynamics, which in turn gives rise to the concept of functional network structure. Here, functional structure refers to groups of neurons that act together to perform a specific function or task. In this dissertation, I address the issue of relating anatomical structure, dynamics, and functional structure in neuronal networks. These three network features are coupled through various dynamic properties which I explore using a variety of methods. I first present a novel algorithm called the Functional Clustering Algorithm which was designed to extract functional groupings from discrete event data. This algorithm provides a method for linking network dynamics with functional structure. I then show applications of the algorithm to experimental and model derived data to explore functional changes as a result of memory consolidation and learning. The application to model derived data allows for a comparison of the dynamics and resulting functional structure with known anatomic structural changes occurring in the model through spike timing dependent plasticity. Next, I explore a system of two coupled networks as a model of focal epilepsy and show that network properties of neurons such as excitability can also drive dynamics, indicating that dynamics and functional structure cannot always be so easily linked to anatomical structure. Finally, I use a reduced system of dissociated hippocampal cultures to simultaneously study the relationship between observed structural differences, network dynamics, and functional structure in cultures with either a high or low density of glial cells
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