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Related Experiment Videos

Is there something out there? Inferring space from sensorimotor dependencies.

D Philipona1, J K O'Regan, J-P Nadal

  • 1Sony CSL, 75005 Paris, France. david.philipona@polytechnique.org

Neural Computation
|September 10, 2003
PubMed
Summary
This summary is machine-generated.

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Brains may construct our reality by learning input-output dependencies. This study demonstrates how a simulated brain can deduce the dimensionality of physical space from its sensory data.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Perception of reality, including concepts like body, environment, and space, is fundamental to biological organisms.
  • The brain's processing of sensory information and its relationship to actions are key areas of study.

Purpose of the Study:

  • To propose that the brain's construction of reality stems from its need to model input-output dependencies efficiently.
  • To demonstrate a computational method for a simulated brain to infer the dimensionality of physical space.

Main Methods:

  • A simulated organism with arbitrary input-output connectivity was used.
  • A procedure was developed to allow the simulated brain to deduce the dimensionality of the underlying rigid group of its input-output relationship.

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Main Results:

  • The procedure successfully enabled the simulated brain to determine the dimension of what it would perceive as physical space.
  • This validates the hypothesis that perceived spatial structure can emerge from modeling input-output dependencies.

Conclusions:

  • The brain's perception of reality, including physical space, may be an emergent property of efficiently modeling sensory input and motor output.
  • This framework offers a computational perspective on how biological organisms construct their understanding of the world.