Abstract
Neuroanatomical data describing the numerous connections between brain structures contain valuable information about the organization of nervous systems. This information, however, cannot be assessed readily since the data are numerous, confusingly cross-referential, incomplete, contradictory, and of varying reliability. The classification of such data, moreover, allows vast numbers of different, equally possible interpretations that have to be evaluated. We have developed a computational approach that effectively deals with these difficulties by using stochastic optimization. We represented cortical connectivity data as ‘black-box’ objects that are linked with each other through a network of anatomical relations. This network can be arranged optimally according to suspected structuring principles. The approach makes it possible to analyze large amounts of complex anatomical data in a number of ways. We have successfully applied this technique to the analysis of processing clusters and hierarchies in cat and monkey cortical systems.
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© 1997 Springer Science+Business Media New York
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Hilgetag, C.C., O’Neill, M.A., Young, M.P. (1997). Optimization Analysis of Complex Neuroanatomical Data. In: Bower, J.M. (eds) Computational Neuroscience. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9800-5_143
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DOI: https://doi.org/10.1007/978-1-4757-9800-5_143
Publisher Name: Springer, Boston, MA
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