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Convolutional Representation in Brain Simulation to Store and Analyze Information About the Surrounding Environment for the Needs of Decision Making

In: Neuroeconomic and Behavioral Aspects of Decision Making

Author

Listed:
  • Mariusz Borawski

    (West Pomeranian University of Technology Szczecin)

Abstract

The chapter describes the capability to utilize convolutional representation in brain simulations to facilitate the storage and analysis of information about the surrounding environment. The convolutional representation allows for easy storage and updating of statistical information about objects. For each number in convolution representation a mean value and variance can be calculated. They can be used to describe objects in 3D space. In this chapter a transformation method of such vectors is presented. Moreover, a method for comparing two numbers in a convolution representation based on mean and variance is shown. The chapter also describes an example of the simulation, where convolutional representation and ordered pairs; mean value, variance increment were utilized. It is the one of possible examples how the presented methods can be used. These methods were developed for data analysis in assessing public awareness campaigns to take account of the uncertainty of respondents’ responses in questionnaires.

Suggested Citation

  • Mariusz Borawski, 2017. "Convolutional Representation in Brain Simulation to Store and Analyze Information About the Surrounding Environment for the Needs of Decision Making," Springer Proceedings in Business and Economics, in: Kesra Nermend & Małgorzata Łatuszyńska (ed.), Neuroeconomic and Behavioral Aspects of Decision Making, chapter 0, pages 91-105, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-62938-4_7
    DOI: 10.1007/978-3-319-62938-4_7
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