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Related Experiment Video

Updated: Jun 5, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Search in unknown random environments.

Erol Gelenbe1

  • 1Department of Electrical and Electronic Engineering, Imperial College, London SW7 2BT, United Kingdom. e.gelenbe@imperial.ac.uk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
PubMed
Summary
This summary is machine-generated.

This study models an infinite search space with multiple searchers, each having a limited lifespan and facing potential failures. It derives a formula for average search time, considering factors like searcher number and failure rates.

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Last Updated: Jun 5, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models
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Area of Science:

  • Probability Theory
  • Stochastic Processes
  • Search Theory

Background:

  • Searching for a fixed object in an infinite space presents challenges due to unknown distance and searcher limitations.
  • Searchers have finite random lifetimes and can fail, requiring replacement by the source.
  • Information guiding the search may be partial and available only at intermediate locations.

Purpose of the Study:

  • To derive a closed-form expression for the average search time in an infinite search space.
  • To analyze the impact of various parameters on search efficiency and cost.
  • To model a scenario with multiple independent searchers with finite lifetimes and potential failures.

Main Methods:

  • Utilized N coupled Brownian motions to model the search process.
  • Derived a mathematical expression for average search time as a function of distance (D).
  • Examined the total energy expenditure as a measure of search cost.

Main Results:

  • A closed-form expression for average search time was obtained, dependent on key parameters.
  • The model accounts for the number of searchers, their average lifetime, routing uncertainty, and failure rates.
  • Analysis included the total energy cost associated with the search operations.

Conclusions:

  • The derived formula provides a quantitative understanding of search time in complex scenarios.
  • Search efficiency is significantly influenced by searcher characteristics and environmental factors.
  • The study offers insights into optimizing search strategies and resource allocation in infinite search spaces.