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Of maps and grids.

Matteo Grasso1, Andrew M Haun1, Giulio Tononi1

  • 1Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.

Neuroscience of Consciousness
|September 24, 2021
PubMed
Summary
This summary is machine-generated.

This study models how brain networks represent spatial information for visual fixation. It shows that grid-like networks, unlike map-like ones, can explain the subjective experience of space using Integrated Information Theory.

Keywords:
consciousnesscontents of consciousnessfunctionalismintegrated information theoryspace

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Traditional neuroscience explains brain function through information processing.
  • This approach often overlooks the subjective, qualitative aspects of conscious experience.
  • Understanding the neural basis of subjective experience, like the feeling of 'seeing' at a location, remains a challenge.

Purpose of the Study:

  • To model how neural networks represent spatial information and generate actions like visual fixation.
  • To investigate whether functional models can account for the subjective properties of spatial experience.
  • To explore the utility of Integrated Information Theory in bridging objective neural mechanisms and subjective phenomenal properties.

Main Methods:

  • Developed a computational model of a "grid-like" neural network, simulating posterior cortical areas.
  • Utilized standard neuroscience tools to analyze the model's representation of retinal stimulus position and control of eye muscles.
  • Applied the framework of Integrated Information Theory to analyze the model's "cause-effect structure".

Main Results:

  • The grid-like model successfully represented spatial information and performed fixation functions, demonstrating encoding, decoding, and tuning properties.
  • Functional analysis alone did not explain the subjective experience of "seeing" at a location.
  • Integrated Information Theory analysis of the grid-like network's cause-effect structure provided an objective account for the subjective experience of spatial extendedness.

Conclusions:

  • Grid-like neural networks, through their specific cause-effect structures, can computationally account for the subjective experience of space.
  • Functionally equivalent map-like networks lacking lateral connections fail to explain these phenomenal properties.
  • This work suggests a pathway for integrating objective neural mechanisms with subjective conscious experience using theoretical frameworks like Integrated Information Theory.