Book Review: Networks of the Brain

  title={Book Review: Networks of the Brain},
  author={Fozia Anwar and Afifa Yousafzai and Muaz A. Niazi},
  journal={Frontiers in Psychology},
Natural systems consisting of living systems as well as some artificial systems (Altamimi and Ramadan, 2016; Batool and Niazi, 2017) can be classified as Complex Adaptive Systems (CAS) (Holland, 2012). These systems have complexity in terms of having nonlinear interactions between numerous agents—interactions which cannot be easily summarized. CAS also have some rather peculiar properties—they adapt such that individual agents do not matter—an example being of cells which can continually get… 
1 Citations
Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson’s arms race model
The concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior, and the performance of the proposed social agent has been validated at two levels.


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This book is not a textbook, but rather an essay on complex adaptive systems, and the best method to discover their properties is to dispatch many computer agents to experience the system’s possibilities.
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This work uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks and demonstrates modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks.
Towards internet of things modeling: a gateway approach
The paper concludes that employing mobile ferries as gateways is better than deploying gateways based on geographical area when the sub networks interaction is facilitated in IoT network.
Analysis and visualization of large networks with program package Pajek
Pajek is a program package for analysis and visualization of large networks (networks containing up to one billion of vertices, there is no limit—except the memory size—on the number of lines). It
Multi-Agent Foraging: state-of-the-art and research challenges
This paper investigates the Multi-Agent Foraging problem from several perspectives that are analyzed in depth, and proposes a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation.