Complex Networks: Structure and Function of Large-Scale Neural Networks

Abstract

A two-state master equation (TSME), the decision making model(DMM), was developed and shown to generate phase transitions, to be topologically complex and to manifest temporal complexity through an inverse power-law (IPL) probability distribution function(PDF) in the switching times between the two critical states of consensus. These properties are entailed by the fundamental assumption that the network elements in the DMM imperfectly imitate one another. The process of subordination establishes that a single network element can be described by a fractional master equation(FME), whose analytic solution yields the observed IPL PDF, obtained by numerical integration of the TSME to a high degree of accuracy. The network dynamics describe the criticality of neuronal and genetic systems.

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Document Details

Document Type
Technical Report
Publication Date
Aug 03, 2016
Accession Number
AD1072117

Entities

People

  • Bruce J. West
  • Malgorzata Turalska

Organizations

  • Duke University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Complex Systems
  • Department Of Defense
  • Differential Equations
  • Distribution Functions
  • Engineering
  • Equations
  • Law
  • Mathematics
  • Military Research
  • Network Science
  • Nonlinear Differential Equations
  • Phase Transformations
  • Physics
  • Probability
  • Probability Distribution Functions
  • Probability Distributions
  • Students

Readers

  • Graph Algorithms and Convex Optimization.
  • Immunology
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology