Communication Properties of Self-Organizing Networks (SWARMS) as Inferred from Optical Mechanics

Abstract

A primary question in our research is to determine how much sensory processing is necessary to maintain a network's resiliency to fission, or collapse, while minimizing the degree of disorder in communication. Biological components capable of governing these processes include perception, thresholds, and simple decision filters. We present a self-propelled particle model (s.p.p.) with simplified optical rules in which individual interactions are dictated by retinal representations of object bearing and motion. Optical signals are weighted by individual perception and the degree of redundant neurological stimulation. In turn, signals that exceed a threshold value serve to identify influential neighbors within the swarm's global network. Individual connections (i.e., each influential neighbor) is normalized to the individual's immediate surroundings, making the decision process adaptive to both physical and behavioral variability.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2008
Accession Number
ADA505899

Entities

People

  • Bertrand H. Lemasson
  • James J. Anderson
  • R. A. Goodwin

Organizations

  • University of Washington

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Animals
  • Cells
  • Computational Science
  • Control Systems
  • Detection
  • Detectors
  • Diseases And Disorders
  • Engineering
  • Fish
  • Mechanics
  • Models
  • Network Science
  • Particle Swarm Optimization
  • Particles
  • Perception
  • Self Propelled

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference