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On human consciousness: A mathematical perspective.

Peter Grindrod1

  • 1Mathematical Institute, University of Oxford, United Kingdom.

Network Neuroscience (Cambridge, Mass.)
|February 23, 2019
PubMed
Summary
This summary is machine-generated.

Large modular neural networks exhibit nonbinary information processing, enabling complex decision-making. This model explains how feelings arise in consciousness and confer an evolutionary advantage for fast thinking.

Keywords:
Bayesian nonbinary processingConsciousnessDual hierarchyLatent variablesStrongly connected delay networks

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Area of Science:

  • Computational neuroscience
  • Mathematical modeling of neural networks
  • Philosophy of mind

Background:

  • Understanding the brain's architecture and information processing is key to consciousness.
  • Nonbinary information processing is proposed as a fundamental mechanism in neural networks.
  • The "hard problem of consciousness" remains a significant challenge in neuroscience and philosophy.

Purpose of the Study:

  • To explore the implications of mathematical modeling on large modular neuron-to-neuron dynamical networks.
  • To investigate how nonbinary information processing in neural networks relates to decision-making and consciousness.
  • To propose a "dual hierarchy model" for understanding subjective feelings and evolving consciousness.

Main Methods:

  • Mathematical modeling and analysis of large modular neuron-to-neuron dynamical networks.
  • Development of a "dual hierarchy model" incorporating physical and mental elements.
  • Application of dynamical systems theory and nonbinary information processing concepts.

Main Results:

  • Dynamical behavior in small, strongly connected networks naturally leads to nonbinary information processing and multiple hypothesis decision-making.
  • The proposed model suggests feelings arise from a foundational decision-making layer of unit processors.
  • Finite human brains are characterized by continuous learning and forgetting, with subjective feelings evolving over time.

Conclusions:

  • Mental elements (feelings) function as latent variables, providing an evolutionary "fast-thinking" advantage.
  • Consciousness and subjective feelings are supported by a dual hierarchy model of physical and mental elements.
  • Complete a priori learning of idealized subjective feelings by automata is impossible; they are experienced by evolving brains.