Artificial Intelligence’s Foundation for Neural Correlates of Consciousness

Authors

  • Vijendra Kumar Maurya Associate Professor, Department of Computer Science and Engineering, Geetanjali Institute of Technical Studies, Udaipur, Rajasthan, India
  • Payal Sachdev Assistant Professor, Department of Computer Science and Engineering, Geetanjali Institute of Technical Studies, Udaipur, Rajasthan, India
  • Rakshit Kothari Assistant Professor, Department of Computer Science and Engineering, Geetanjali Institute of Technical Studies, Udaipur, Rajasthan, India
  • Narendra Singh Rathore Campus Director, Geetanjali Institute of Technical Studies, Udaipur, Rajasthan, India

Keywords:

consciousness, neural correlates, neurons, Artificial Intelligence

Abstract

Consciousness is an aggregation and multidimensional phenomenon that has been the subject of intense research across various disciplines. In recent years, the field of computational neuroscience has made significant strides towards understanding the underlying mechanisms of consciousness and its potential implementation in machines. The neural network correlates of consciousness are important elements of neuronal aggregates that are involved in conscious awareness but are not uniformly distributed throughout the central nervous system. This paper presents a study of the current state of research on consciousness in computation, including an overview of the neural correlates of consciousness hypothesis, deep learning approaches, and the potential implications for fields such as artificial intelligence, robotics, and virtual reality. This paper additionally discusses a recent study showing that the structural intrinsic correlates of consciousness are primarily constrained to a posterior cortical hot zone that comprises sensory regions, as contrast to a frontoparietal services and associated in task tracking and reporting.

References

Menzies T, Greenwald J, Frank A. Data mining static code attributes to learn defect predictors. IEEE Trans Softw Eng. 2007; 33 (1): 2–13.

Dai H. Imbalanced protein data classification using ensemble FTM-SVM. IEEE Trans Nanobiosci. 2015; 14 (4): 350–359.

Li H, Chung F, Wang S. A SVM based classification method for homogeneous data. Appl Soft Comput. 2015; 36: 228–235.

Benos L, Tagarakis A, Dolias G, Berruto R, Kateris D, Bochtis D. Machine learning in agriculture: a comprehensive updated review. Sensors (Basel). 2021; 21 (11): 3758.

Kothari R, Choudhary N, Jain K. CP-ABE scheme with decryption keys of constant size using ECC with expressive threshold access structure. In: Mathur R, Gupta CP, Katewa V, Jat DS, Yadav N, editors. Emerging Trends in Data Driven Computing and Communications. Stud Autonom Data Driven and Industrial Computing. Singapore: Springer; 2021; pp. 15–36.

Khan F, Kothari R, Patel M, Banoth N. Enhancing non-fungible tokens for the evolution of blockchain technology. In: 2022 IEEE International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, April 7–9, 2022. pp. 1148–1153.

Giri KC, Patel M, Sinhal A, Gautam D. A novel paradigm of melanoma diagnosis using machine learning and information theory. In: 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE), Sathyamangalam, India, April 4–6, 2019. pp. 1–7. doi: 10.1109/ICACCE46606.2019.9079975.

Lopez V, Fernandez A, Garca S, Palade C, Herrera F. An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inform Sci. 2013; 250: 113–141.

Maurya VK, Mehra RM, Mehra A. Design and analysis of energy efficient OPAMP for rectifier in microscale energy harvesting (solar energy). In: Satapathy S, Bhatt Y, Joshi A, Mishra D, editors. Proceedings of the International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, Volume 439. Singapore: Springer; 2016. Pp. 229–240.

Huang G, Song S, Gupta J, Wu C. Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern. 2014. 44 (12): 2405–2417.

Rathore R, Sharma R, Bhanawat R, Soni P, Soni PR, Sachdev P. Inter-linked platform for campus placement in higher educational institutions of India. Int J Adv Res Computer Sci. 2022; 13 (1): 122–124.

Parvin H, Minaei-Bidgoli B, Alinejad-Rokny H. A new imbalanced learning and dictions tree method for breast cancer diagnosis. J Bionanosci. 2013; 7 (6): 673–678.

Kaur G, Singh H. Data mining techniques for text mining. Indian J Sci Technol. 2016; 9 (44): 1–4.

Khan F, Kothari R, Patel M. Advancements in blockchain technology with the use of quantum blockchain and non-fungible tokens. In: Shrivas MK, Hiran KK, Bhansali A, Doshi R, editors. Advancements in Quantum Blockchain With Real-Time Applications. Hershey, PA: ICI Global; 2022. pp. 199–225.

Published

2023-08-14

Issue

Section

Review Article