| This project is part of a larger project on Computational analysis of spatiotemporal patterns of activity in neuronal networks (CASPAN). Summary of CASPAN: Information processing in the brain is based on spatiotemporal patterns of electrical activity in neuronal networks. Recently introduced experimental techniques allow the monitoring of these patterns in great detail by simultaneously recording of neuronal activity from a large number of locations in the network (e.g., in cortical brain slices and cultured neuronal networks). In order to be able to analyze and interpret the flood of data these new techniques produce, we intend to develop (i) the appropriate mathematical and statistical methods for analyzing spatiotemporal patterns of neuronal activity, and (ii) computational models of neuronal networks to simulate spatiotemporal patterns of activity and understand these patterns in relation to structural and functional connectivity within the network. Goals (i) and (ii) will be pursued in close interaction, whereby the computational models will be used to help to develop statistical methods, and the statistical methods in turn will be used to test whether the model can capture the characteristics of the experimentally observed spatiotemporal patterns. An essential part of (ii) will be the formulation of a stochastic model for the generation of network connectivity and its variation. The new methods and models will be validated with the extensive data we have on spatiotemporal patterns in cortical brain slices and cultured neuronal networks. Ultimately, these methods and models are indispensable in, for example, the screening of mouse mutants in the search for key genes that affect network activity and animal behavior. |
