| The advent of in vitro neural cultures on planar micro-electrode arrays (MEAs) has created the ability to chronically study the computational and dynamic properties of a reduced two-dimensional neuronal network of approximately 25 to 50 thousand neurons. However, even in these reduced networks the complexity is such that experimentation alone is often inadequate to explain network phenomena at cellular levels. A complimentary approach is to create realistic in silico models of these networks that simulate the individual neuronal and network properties of in vitro networks. Since these models allow the detailed analysis from individual cells to network interactions, they can often provide additional insight into the interaction of populations of neurons at a level that is impossible to accomplish using standard in vitro techniques.;The primary focus of this dissertation is to describe a model of these 2D networks that incorporates generalizations and simplifications of properties of living neural tissue and the MEA technology that measures activity in vitro. This model incorporates multiple neuronal cell types, structural connectivity among those cells, and properties of individual cells including synaptic depression, facilitation, and plasticity. In addition, an important aspect of neural systems is their ability to self-regulate the dynamics of their activity. Unlike previous models of these networks, this property and a dynamic amplitude dependent plasticity rule has been included in the model which solves some of the issues with previous models. The result is a model that produces spontaneous and evoked burst patterns at time scales similar to that of in vitro cultures. Development of the self-regulating model has led to a better understanding of synaptic changes underlying network phenomena.;The experimental validation of the model described in the following chapters provides insight into the network changes documented from other experimental protocols. Specifically, the separability of stimulation (and simulated stimulation) using differing MEA channels is shown. In addition, the location of plastic changes are traced when using tetanizing stimulation.;In summary, a self-regulating model able to reproduce many emergent properties of in vitro networks was developed, validated, and used to explain the response to several common stimulation protocols. The model provides an excellent foundation to manipulate and study the network dynamics of plasticity; interpretation of the model output leads to detailed hypothesis and predictions that can be tested within the model and verified against in vitro experiments. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html ). |
