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Neurodynamics in the Sensorimotor Loop: Representing Behavior Relevant External Situations.

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Summary

This study formalizes neurodynamics in sensorimotor loops using neural networks. It introduces "dynamical forms" as internal representations of external situations, potentially forming the basis of memory.

Keywords:
behavior controlmathematical conceptsneural representationsneurodynamicssensorimotor loop

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Dynamical Systems Theory

Background:

  • Cognition is approached via dynamical systems, with brains controlling autonomous systems driven by sensor signals.
  • Neurodynamics within the sensorimotor loop is explored through a formal, mathematical lens.

Purpose of the Study:

  • To formally investigate neurodynamics in the sensorimotor loop of brain-like systems.
  • To introduce and define key concepts: meta-transients, parametric stability, and dynamical forms.

Main Methods:

  • Utilizes time-discrete dynamics of standard neural networks.
  • Employs a fiber space representation for conceptual clarity.
  • Analyzes neurodynamics in three distinct steps.

Main Results:

  • Introduces meta-transients for realistic sensor input effects.
  • Defines parametric stability for sensor input classes yielding similar dynamic behavior.
  • Proposes dynamical forms as internal representations of external situations, serving as a basis for memory.

Conclusions:

  • Dynamical forms are essential internal representatives of behaviorally relevant external situations.
  • Dynamical forms are suggested as the foundation for memory.
  • Neurodynamics is naturally split into vertical (internal) and horizontal (effective) components based on motor activity influence.