An Integration of Deep Learning and Neuroscience for Machine Consciousness

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Ali Mallakin
Ali Mallakin
α West Coast Biomedius

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An Integration of Deep Learning and Neuroscience for Machine Consciousness

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Abstract

Conscious processing is a useful aspect of brain function that can be used as a model to design artificial-intelligence devices. There are still certain computational features that our conscious brains possess, and which machines currently fail to perform those. This paper discusses the necessary elements needed to make the device conscious and suggests if those implemented, the resulting machine would likely to be considered conscious. Consciousness mainly presented as a computational tool that evolved to connect the modular organization of the brain. Specialized modules of the brain process information unconsciously and what we subjectively experience as consciousness is the global availability of data, which is made possible by a non modular global workspace. During conscious perception, the global neuronal work space at parieto-frontal part of the brain selectively amplifies relevant pieces of information. Supported by large neurons with long axons, which makes the long-distance connectivity possible, the selected portions of information stabilized and transmitted to all other brain modules. The brain areas that have structuring ability seem to match to a specific computational problem. The global workspace maintains this information in an active state for as long as it is needed. In this paper, a broad range of theories and specific problems have been discussed, which need to be solved to make the machine conscious. Later particular implications of these hypotheses for research approach in neuroscience and machine learning are debated.

References

60 Cites in Article
  1. Robin Carhart-Harris,Robert Leech,Peter Hellyer,Murray Shanahan,Amanda Feilding,Enzo Tagliazucchi,Dante Chialvo,David Nutt (2014). The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs.
  2. Joseph Fins (2008). Neuroethics and Neuroimaging: Moving Toward Transparency.
  3. G1 Tononi (2005). Consciousness, information integration, and the brain.
  4. B Baars,S Franklin (2009). Consciousness is computational: The LIDA model of global workspace theory.
  5. Bernard Baars (1997). The Global Workspace Theory of Consciousness.
  6. A Revonsuo (2006). Inner presence: Consciousness as a biological phenomenon.
  7. H Haladjian,C Montemayor (2016). Artificial consciousness and the consciousness-attention dissociation.
  8. Jennifer Hunter,Eleenor Abraham,Andrew Hunter,Lauren Goldberg,John Eastwood (2016). Personality and boredom proneness in the prediction of creativity and curiosity.
  9. Wei-Lun Lin,Yi-Ling Shih (2016). The developmental trends of different creative potentials in relation to children’s reasoning abilities: From a cognitive theoretical perspective.
  10. Janusz Starzyk,Dilip Prasad (2011). A COMPUTATIONAL MODEL OF MACHINE CONSCIOUSNESS.
  11. Maciej Karwowski,Jan Dul,Jacek Gralewski,Emanuel Jauk,Dorota Jankowska,Aleksandra Gajda,Michael Chruszczewski,Mathias Benedek (2016). Is creativity without intelligence possible? A Necessary Condition Analysis.
  12. Allison Kaufman,Allen Butt,James Kaufman,Erin Colbert-White (2011). Towards a neurobiology of creativity in nonhuman animals..
  13. Olivier Brabant (2016). More Than Meets the Eye: Toward a Post-Materialist Model of Consciousness.
  14. David Silver,Julian Schrittwieser,Karen Simonyan,Ioannis Antonoglou,Aja Huang,Arthur Guez,Thomas Hubert,Lucas Baker,Matthew Lai,Adrian Bolton,Yutian Chen,Timothy Lillicrap,Fan Hui,Laurent Sifre,George Van Den Driessche,Thore Graepel,Demis Hassabis (2017). Mastering the game of Go without human knowledge.
  15. Brenden Lake,Tomer Ullman,Joshua Tenenbaum,Samuel Gershman (2017). Building machines that learn and think like people.
  16. David Gamez (2008). Progress in machine consciousness.
  17. J Reggia (2013). The rise of machine consciousness: Studying consciousness with computational models.
  18. J Reggia,D Monner,J Sylvester (2014). The computational explanatory gap.
  19. Michael Graziano,Taylor Webb (2014). A Mechanistic Theory of Consciousness.
  20. Michael Graziano (2017). The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness.
  21. F Peters (2013). Theories of Consciousness as Reflexivity.
  22. Avinash De Sousa (2013). Towards an integrative theory of consciousness: Part 1 (Neurobiological and cognitive models).
  23. Graham Peebles (2013). Reflexive theories of consciousness and unconscious perception.
  24. G Edelman (1989). The remembered present.
  25. B Bahrami,K Olsen,P Latham,A Roepstorff,G Rees,C Frith (2010). Optimally interacting minds.
  26. Norbert Kerr,R Tindale (2004). Group Performance and Decision Making.
  27. B Baars (1989). Neurobiology of Cognition. Edited by P. D. Eimas and A. M. Galaburda. (Pp. 250; illustrated; £19.95.) MIT Press: London. 1990. - A Cognitive Theory of Consciousness. By B. J. Baars. (Pp. 424; illustrated; £27.50.) Cambridge University Press: Cambridge. 1989..
  28. S Dehaene (2014). Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts.
  29. Aaron Schurger,Ioannis Sarigiannidis,Lionel Naccache,Jacobo Sitt,Stanislas Dehaene (2015). Cortical activity is more stable when sensory stimuli are consciously perceived.
  30. Pablo Barttfeld,Lynn Uhrig,Jacobo Sitt,Mariano Sigman,Béchir Jarraya,Stanislas Dehaene (2015). Signature of consciousness in the dynamics of resting-state brain activity.
  31. R Quiroga,R Mukamel,E Isham,R Malach,I Fried (2008). Human single-neuron responses at the threshold of conscious recognition.
  32. S Dehaene,J Changeux (2011). Experimental and theoretical approaches to conscious processing.
  33. Jérôme Sackur,Stanislas Dehaene (2009). The cognitive architecture for chaining of two mental operations.
  34. Dan Sperber,Deirdre Wilson (1988). Précis of <i>Relevance: Communication and Cognition</i>.
  35. C Baker,R Saxe,J Tenenbaum (2009). Action understanding as inverse planning.
  36. Jean Daunizeau,Hanneke Den Ouden,Matthias Pessiglione,Stefan Kiebel,Klaas Stephan,Karl Friston (2010). Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making.
  37. C Brown (2005). Remote Viewing: The Science and Theory of Nonphysical Perception.
  38. D Marks (2000). The Psychology of the Psychic (2nd Edition).
  39. A Einstein,B Podolsky,N Rosen (1935). Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?.
  40. T Nagel (1974). What is it like to be a bat?.
  41. F Crick,C Koch (1998). Consciousness and neuroscience.
  42. Francis Crick,Christof Koch (2003). A framework for consciousness.
  43. Joseph Levine (1983). MATERIALISM AND QUALIA: THE EXPLANATORY GAP.
  44. S Dehaene,J Changeux (2011). Experimental and theoretical approaches to conscious processing.
  45. Victor Lamme,Pieter Roelfsema (2000). The distinct modes of vision offered by feedforward and recurrent processing.
  46. Victor Lamme (2006). Towards a true neural stance on consciousness.
  47. Victor Lamme (2010). How neuroscience will change our view on consciousness.
  48. David Rosenthal (1986). Two concepts of consciousness.
  49. D Rosenthal (2005). Consciousness and Mind.
  50. D Rosenthal (2002). Explaining Consciousness.
  51. L Hakwan,D Rosenthal (2011). Empirical Support for Higher-Order Theories of Conscious Awareness.
  52. Benjamin Kozuch (2014). Prefrontal lesion evidence against higher-order theories of consciousness.
  53. Melanie Boly,Marcello Massimini,Naotsugu Tsuchiya,Bradley Postle,Christof Koch,Giulio Tononi (2017). Are the Neural Correlates of Consciousness in the Front or in the Back of the Cerebral Cortex? Clinical and Neuroimaging Evidence.
  54. Giulio Tononi (2004). An information integration theory of consciousness.
  55. Giulio Tononi (2008). Consciousness as Integrated Information: a Provisional Manifesto.
  56. G Tononi (2015). Integrated Information Theory.
  57. G Edelman,G Tononi (2000). A universe of consciousness: How matter becomes imagination. A universe of consciousness: How matter becomes imagination.
  58. Giulio Tononi,Melanie Boly,Marcello Massimini,Christof Koch (2016). Integrated information theory: from consciousness to its physical substrate.
  59. Sarah Eagleman,David Drover (2018). Calculations of consciousness.
  60. Hyoungkyu Kim,Anthony Hudetz,Joseph Lee,George Mashour,Uncheol Lee (2018). Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Ali Mallakin. 2019. \u201cAn Integration of Deep Learning and Neuroscience for Machine Consciousness\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D1): .

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Issue Cover
GJCST Volume 19 Issue D1
Pg. 21- 29
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-D Classification: I.2.6
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v1.2

Issue date

March 26, 2019

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en
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Conscious processing is a useful aspect of brain function that can be used as a model to design artificial-intelligence devices. There are still certain computational features that our conscious brains possess, and which machines currently fail to perform those. This paper discusses the necessary elements needed to make the device conscious and suggests if those implemented, the resulting machine would likely to be considered conscious. Consciousness mainly presented as a computational tool that evolved to connect the modular organization of the brain. Specialized modules of the brain process information unconsciously and what we subjectively experience as consciousness is the global availability of data, which is made possible by a non modular global workspace. During conscious perception, the global neuronal work space at parieto-frontal part of the brain selectively amplifies relevant pieces of information. Supported by large neurons with long axons, which makes the long-distance connectivity possible, the selected portions of information stabilized and transmitted to all other brain modules. The brain areas that have structuring ability seem to match to a specific computational problem. The global workspace maintains this information in an active state for as long as it is needed. In this paper, a broad range of theories and specific problems have been discussed, which need to be solved to make the machine conscious. Later particular implications of these hypotheses for research approach in neuroscience and machine learning are debated.

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An Integration of Deep Learning and Neuroscience for Machine Consciousness

Ali Mallakin
Ali Mallakin West Coast Biomedius

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