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Optimization Analysis of Complex Neuroanatomical Data

  • Claus C. Hilgetag
  • Mark A. O’Neill
  • Malcolm P. Young

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.

Keywords

Anatomical Data Optimization Analysis Cortical Visual Area Neural Activation Network Area Arrangement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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
  2. 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
  3. D J Felleman, D C Van Essen: Distributed Hierarchical Processing in the Primate Cerebral Cortex, Cerebral Cortex 1 (1991), 1–47.PubMedCrossRefGoogle Scholar
  4. 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
  5. C C Hilgetag, M A O’Neill, M P Young: Indeterminate Organization of the Visual System, Science 271 (1996), 776–777.PubMedCrossRefGoogle Scholar
  6. P J M Van Laarhoven, E H L Aarts: Simulated Annealing. - Theory and Applications, Kluwer: Dordrecht 1987.Google Scholar
  7. K S Rockland, D N Pandya: Laminar Origins and Terminations of Cortical Connections of the Occipital Lobe in the Rhesus Monkey, Brain Res 179 (1979), 3–20.PubMedCrossRefGoogle Scholar
  8. J W Scannell, C Blakemore, M P Young: Analysis of Connectivity in the Cat Cerebral Cortex, J Neurosci 15 (1995), 1463–1483.PubMedGoogle Scholar
  9. M P Young: Objective Analysis of the Topological Organization of the Primate Cortical Visual System, Nature 358 (1992), 152–155.PubMedCrossRefGoogle Scholar
  10. M P Young, J W Scannell, M A O’Neill, C C Hilgetag, G Burns, C Blakemore: Non-metric Multidimensional Scaling in the Analysis of Neuroanatomical Connection Data and the Organization of the Primate Cortical Visual System, Phil Trans R Soc Lond B 348 (1995), 281–308.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Claus C. Hilgetag
    • 1
  • Mark A. O’Neill
    • 1
  • Malcolm P. Young
    • 1
  1. 1.Department of PsychologyNeural Systems Group, University of Newcastle upon TyneNewcastle upon TyneUK

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