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Perspectives on Neuroscience
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Slow dynamics perspectives on the Embodied-Brain Systems Science.

Shiro Yano1, Takaki Maeda2, Toshiyuki Kondo1

  • 1Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, 2-24-16 Nakachou, Koganei-shi, Tokyo 184-8588, Japan.

Neuroscience Research
|December 9, 2015
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Summary

This study explores fast-slow dynamical systems and Bayesian inference, highlighting their mathematical underpinnings. It offers a novel fast-slow perspective on Bayesian inference for cognitive and self-body learning processes.

Keywords:
Bayesian statisticsDimension reductionDynamical systemEmbodied-Brain Systems ScienceFast-slow systemsStatistical learning theory

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Mathematical Physics

Background:

  • Fast-slow dynamical systems are crucial for understanding complex behaviors across various scientific fields.
  • Bayesian perspectives offer valuable insights into cognitive systems and learning.
  • Coherent behavior in multi-degree-of-freedom systems can often be simplified using a few key variables.

Purpose of the Study:

  • To review the mathematical foundations of fast-slow dynamical systems.
  • To review the principles of Bayesian statistics.
  • To introduce a novel fast-slow perspective on Bayesian inference.

Main Methods:

  • Literature review of mathematical theories for fast-slow dynamical systems.
  • Literature review of Bayesian statistical principles.
  • Conceptual synthesis of fast-slow dynamics and Bayesian inference.

Main Results:

  • The mathematical basis for fast-slow dynamical systems is elucidated.
  • The foundational concepts of Bayesian statistics are presented.
  • A framework for applying fast-slow perspectives to Bayesian inference is proposed.

Conclusions:

  • Fast-slow dynamical systems provide a powerful mathematical framework for complex phenomena.
  • Bayesian inference is a key tool in understanding cognitive processes.
  • Integrating fast-slow dynamics with Bayesian inference offers new avenues for research in cognitive science and self-body learning.