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Maximal Connectivity and Constraints in the Human Brain

  • Roman V. BelavkinEmail author
Chapter
  • 970 Downloads
Part of the Fields Institute Communications book series (FIC, volume 63)

Abstract

We represent neural networks by directed graphs and consider the problem of maximal connectivity with constraints. This problem is motivated by some conflicting objectives in the design of biological neural networks. Inequalities and equations derived are tested on data and numerical estimates for parameters of a human brain. Results support an intuition that human brain is maximally connected subject to constraints on in- and out-degrees.

Keywords

Cranial Nerve Directed Graph Peripheral Nervous System Hide Node Spinal Nerve 
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.

Notes

Acknowledgements

This work was supported in part by EPSRC grant EP/DO59720.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Engineering and Information SciencesMiddlesex UniversityLondonUK

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