An Integration of Deep Learning and Neuroscience for Machine Consciousness

Ali Mallakin

An Integration of Deep Learning and Neuroscience for Machine Consciousness

Ali Mallakin * §

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§ West Coast Biomedius

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.

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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. "An Integration of Deep Learning and Neuroscience for Machine Consciousness". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D1).

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GJCST Volume 19 Issue D1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification I.2.6

Submission ReceivedDecember 13, 2018
Peer Review Double Blind
Handling Editor
Accepted January 1, 2019
Published January 15, 2019

Version of record

v1.2

Issue date

March 26, 2019

Language

English

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Article Matrices

Total Score: 171

Country: Canada

Subject: Global Journal of Computer Science and Technology

Authors: Ali Mallakin (PhD/Dr. count: 0)

View Count (all-time): 386

Total Views (Real + Logic): 1644

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Publish Date: 2019 01, Tue

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