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NEOCORTEX'S ARCHITECTURE OPTIMIZES COMPUTATION, INFORMATION TRANSFER AND SYNCHRONIZABILITY, AT GIVEN TOTAL CONNECTION LENGTH
Journal article

NEOCORTEX'S ARCHITECTURE OPTIMIZES COMPUTATION, INFORMATION TRANSFER AND SYNCHRONIZABILITY, AT GIVEN TOTAL CONNECTION LENGTH

  • STOOP, R. Institute of Neuroinformatics, ETH/University of Zürich, Winterthurerstr. 190, CH-8057 Zürich, Switzerland
  • WAGNER, C. Institute of Neuroinformatics, ETH/University of Zürich, Winterthurerstr. 190, CH-8057 Zürich, Switzerland
  • 2011-11-20
Published in:
  • International Journal of Bifurcation and Chaos. - World Scientific Pub Co Pte Lt. - 2007, vol. 17, no. 07, p. 2257-2279
Modelling and Simulation
Applied Mathematics
English It is experimental evidence that biological neocortical neurons are arranged in a columnar clustered architecture and coupled according to a bi-power law connection probability function. We propose a computational framework that relies on recurrent connectivity and work out associated computational properties. Taking the columns as the basic computational elements, we investigate a bi-power connection probability function paradigm, in order to scan a wide range of network types, for which we measure the speed of information propagation and synchronizability. Whereas the speed of information propagation increases linearly in the neighbor order n for n-nearest neighbor coupled networks, for close-to-biology bi-power models of the cortex it quickly saturates at high values, expressing the superiority of this network type. Our results reveal that these networks optimize information propagation and synchronizability at a minimal total connection length.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/187674
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