A probabilistic study of neural complexity

Abstract : G. Edelman, O. Sporns, and G. Tononi have introduced the neural complexity of a family of random variables, defining it as a specific average of mutual information over subfamilies. We show that their choice of weights satisfies two natural properties, namely exchangeability and additivity, and we call any functional satisfying these two properties an intricacy. We classify all intricacies in terms of probability laws on the unit interval and study the growth rate of maximal intricacies when the size of the system goes to infinity. For systems of a fixed size, we show that maximizers have small support and exchangeable systems have small intricacy. In particular, maximizing intricacy leads to spontaneous symmetry breaking and failure of uniqueness.
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Preprints, Working Papers, ...
minor edits. 2009
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Contributor : Jerome Buzzi <>
Submitted on : Friday, December 18, 2009 - 10:19:03 PM
Last modification on : Wednesday, January 4, 2017 - 4:19:48 PM
Document(s) archivé(s) le : Thursday, September 23, 2010 - 6:14:55 PM


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  • HAL Id : hal-00409143, version 3
  • ARXIV : 0908.1006



Jerome Buzzi, Lorenzo Zambotti. A probabilistic study of neural complexity. minor edits. 2009. 〈hal-00409143v3〉



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