Introduction to the Fractality Principle of Consciousness and the Sentyon Postulate

Erhard Bieberich

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Recently, consciousness research has gained much attention. Indeed, the question at stake is significant: Why is the brain not just a computing device, but generates a perception from within? Ambitious endeavors trying to simulate the entire human brain assume that the algorithm will do the trick: as soon as we assemble the brain in a computer and increase the number of operations per time, consciousness will emerge by itself. I disagree with this simplistic representation. My argument emerges from the "atomism paradox" the irreducible space of the consciously perceived world, the endospace is incompatible with the reducible and decomposable architecture of the brain or a computer. I will first discuss the fundamental challenges in current consciousness models and then propose a new model based on the fractality principle: "the whole is in each of its parts." This new model copes with the atomism paradox by implementing an iterative mapping of information from higher-order brain structures to smaller scales on the cellular and molecular level, which I will refer to as "fractalization." This information fractalization gives rise to a new form of matter that is conscious ("bright matter"). Bright matter is composed of conscious particles or units named "sentyons." The internal fractality of these sentyons will close a loop (the "psychic loop") in a recurrent fractal neural network (RFNN) that allows for continuous and complete information transformation and sharing between higher-order brain structures and the endpoint substrate of consciousness at the molecular level.

Original languageEnglish (US)
Pages (from-to)13-28
Number of pages16
JournalCognitive Computation
Volume4
Issue number1
DOIs
StatePublished - Mar 1 2012

Keywords

  • Fractal
  • Global workspace
  • Information sharing
  • Network
  • Recurrent

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Cognitive Neuroscience

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