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BSDT Atom of Consciousness Model, AOCM: The Unity and Modularity of Consciousness

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Book cover Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5769))

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Abstract

At the ground of most brain computations may be minimal abstract selectional machines (ASMs) implementing optimal algorithms of recent binary signal detection theory (BSDT). Using the BSDT ASMs, such fundamental cognitive notions as subjectivity and the meaning of a message have already been defined mathematically. BSDT neural network assembly memory model provides strict and biologically plausible definition of optimal assembly memory units (AMUs, implementations of ASMs) which may be considered as ‘atoms’ of consciousness (AOCs). The idea of an AOC is here developed into an ‘atom’ of consciousness model (AOCM) — a mathematical theory of consciousness. Neuronal computational structures leading to the emergence of subjective experience or a ‘quale’ (a formal solution of the ‘hard problem’ of consciousness) are presented as complex dynamical hierarchical associations of AMUs/AOCs of infinite prehistory. Within the AOCM framework some cognitive phenomena are explained and it has been demonstrated that unified and modular biological models of consciousness are not antithetical.

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Author information

Authors and Affiliations

  1. Universal Power Systems USA-Ukraine LLC, 3 Kotsarskaya Street, Kharkiv, 61012, Ukraine

    Petro Gopych

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  1. Petro Gopych
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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Gopych, P. (2009). BSDT Atom of Consciousness Model, AOCM: The Unity and Modularity of Consciousness. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-04277-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04276-8

  • Online ISBN: 978-3-642-04277-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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