Optimization Analysis of Complex Neuroanatomical Data
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.
KeywordsAnatomical Data Optimization Analysis Cortical Visual Area Neural Activation Network Area Arrangement
Unable to display preview. Download preview PDF.
- G A P C Burns, M A O’Neill, M P Young: Calculating Finely-graded Ordinal Weights for Neural Connections from Neuroanatomical Data from Different Anatomical Studies. In Computational Neuroscience `96 (ed. J. M. Bower ). Boston: Plenum 1997.Google Scholar
- S Ramón y Cajal: New ideas on the Structure of the Nervous System in Man and Vertebrates. Translated by N Swanson and L W Swanson. MIT Press: Cambrigde MA 1990.Google Scholar
- C C Hilgetag, M A O’Neill, J W Scannell, M P Young: A Novel Network Classifier and its Application: Optimal Hierarchical Orderings of the Cat Visual System from Anatomical Data, Genetic Algorithms in Engineering Systems: Innovations and Applications, lEE Publication No. 414, Sheffield 1995.Google Scholar
- P J M Van Laarhoven, E H L Aarts: Simulated Annealing. - Theory and Applications, Kluwer: Dordrecht 1987.Google Scholar