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.
Mathematics Subject Classification (2010): Primary 94C15; Secondary 92C20
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This work was supported in part by EPSRC grant EP/DO59720.
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Belavkin, R.V. (2013). Maximal Connectivity and Constraints in the Human Brain. In: Pardalos, P., Coleman, T., Xanthopoulos, P. (eds) Optimization and Data Analysis in Biomedical Informatics. Fields Institute Communications, vol 63. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4133-5_10
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DOI: https://doi.org/10.1007/978-1-4614-4133-5_10
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