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Algorithmic Self-Instructing Consciousness | Skopec | NeuroQuantology


DOI: 10.14704/nq.2016.14.4.967

Algorithmic Self-Instructing Consciousness

Robert Skopec

Abstract


Let we are outgoing from the thesis: if consciousness corresponds to the capacity to integrate information, then the system should be able to generate consciousness to the extent having a large repertoire of available states (information). Natural selection is an algorithm for generating adaptation and the question is, whether it may be utilized for cognition. Natural selection is capable to improve itself as a heuristic search algorithm. In neuronal information self-transfer is possible formation of a one-to-one topographic map between two neuronal layers, and reconstruction of the intra-layer topology of the parent in the offspring layer. The problem of neuronal transfer exists, from anatomical (activity-dependent) mechanisms, to self-instructing (activity-independent) algorithms. We establish a link between network topology and information integration showing how biologically inspired auto-adaptation improves the consciousness self-instructing.

Keywords


algorithm; auto-adaptation; self-organization; consciousness; integrated information theory

Full Text:

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References


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Supporting Agencies

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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