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Object-Based Neural Model in Multicore Environments with Improved Biological Plausibility

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Computational Vision and Bio-Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1318))

Abstract

It is generally known that the computational neuroscience is usually considered as a mathematics-driven model. Computational neuroscience maps the functions of the neural populations to computer-based mathematical functions. Presently, numerous efforts have been taken to provide neural models that mimic the biological functions of neurons. This endeavor suggests the need for deploying comprehensive object-based basic neural models in the place of mathematical models that has been implemented by just using object-oriented programming languages. This paper describes the object-based model (OBM) and how it is different from the models using object-oriented languages. The consistent premise ‘Everything is not learning’ is set and is justified by the biological supports produced here. This paper also discusses about the alternate methods adopted by various researchers to prove that only a complete object-based design of neurons will have convergence toward the objective that mimic the actions of brain. This paper analyzes the need for different programming paradigms and concludes with a suggestion that the adaptive programming language ‘Go’ is a language that suits the implementation of such a model.

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References

  1. A New Supercomputer Is the World’s Fastest Brain-Mimicking Machine, https://www.scientificamerican.com/article/a-new-supercomputer-is-the-worlds-fastest-brain-mimicking-machine/

  2. S. Legg, M. Hutter, A collection of definitions of intelligence. Frontiers Artif. Intell. Appl. 157, 17 (2007)

    Google Scholar 

  3. S. Legg, M. Hutter, A Formal Measure of Machine Intelligence. arXiv preprint cs/0605024 (2006)

    Google Scholar 

  4. N. Schwarz, Emotion, cognition, and decision making. Cogn. Emot. 14(4), 433–440 (2000)

    Article  Google Scholar 

  5. K. Shyamala, P. Chanthini, R. Krishnan, A. Murugan, Artificial neural network model adopting combinatorial inhibition process in multiple solution problems. Int. J. Eng. Technol. 7(3.4), 167–173 (2018)

    Google Scholar 

  6. K. Shyamala, P. Chanthini, R. Krishnan, A. Murugan, Adoption of combinatorial graph for inhibitory process in optimization problems. Int. J. Appl. Eng. Res. 13(13), 11261–11266 (2018)

    Google Scholar 

  7. M. Saber, A. El Rharras, R. Saadane, H.K. Aroussi, M. Wahbi, Artificial neural networks, support vector machine and energy detection for spectrum sensing based on real signals. Int. J. Commun. Netw. Inf. Sec. 11(1), 52–60 (2019)

    Google Scholar 

  8. D. Thukaram, H.P. Khincha, H.P. Vijaynarasimha, Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans. Power Deliv. 20(2), 710–721 (2005)

    Article  Google Scholar 

  9. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning. MIT press (2016)

    Google Scholar 

  10. S. Jürgen,Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Google Scholar 

  11. Researchers Develop Device that Mimics Brain Cells Used for Human Vision, https://phys.org/news/2020-02-device-mimics-brain-cells-human.html

  12. Beyond Deep Learning—3rd Generation Neural Nets, https://www.datasciencecentral.com/profiles/blogs/beyond-deep-learning-3rd-generation-neural-nets

  13. Spiking Neural Networks, The Next Generation of Machine Learning, https://towardsdatascience.com/spiking-neural-networks-the-next-generation-of-machine-learning-84e167f4eb2b

  14. N. Kasabov, L. Benuskova, S.G. Wysoski, A computational neurogenetic model of a spiking neuron, in Proceedings. IEEE International Joint Conference on Neural Networks, vol. 1 (IEEE, 2005)

    Google Scholar 

  15. S. Ghosh-Dastidar, H. Adeli, Third Generation Neural Networks: Spiking Neural Networks. Advances in Computational Intelligence. Springer Berlin, Heidelberg (2009), 167–178

    Google Scholar 

  16. P.A. Merolla, J.V. Arthur, R. Alvarez-Icaza, A.S. Cassidy, J. Sawada, F. Akopyan, B. Brezzo, A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014)

    Article  Google Scholar 

  17. Scientists Want to Mimic the Human Brain and They’ve Made a Breakthrough https://www.weforum.org/agenda/2016/10/scientists-want-to-mimic-the-human-brain-and-they-ve-made-a-breakthrough/

  18. J. Grollier, D. Querlioz, M.D. Stiles, pintronic nanodevices for bioinspired computing. Proc. IEEE 104(10), 2024–2039 (2016)

    Article  Google Scholar 

  19. A. Baddeley, Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29 (2012)

    Article  Google Scholar 

  20. Y. Hao, X. Huang, M. Dong, B. Xu, A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule. Neural Netw 121, 387–395 (2020)

    Article  Google Scholar 

  21. J. Choi, M. Ahn, J.T. Kim, Implementation of hardware model for spiking neural network, in Proceedings on the International Conference on Artificial Intelligence (ICAI). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2015), p. 700

    Google Scholar 

  22. D. Sarkar, J. Tao, W. Wang, Q. Lin, M. Yeung, C. Ren, R. Kapadia, Mimicking biological synaptic functionality with an indium phosphide synaptic device on silicon for scalable neuromorphic computing. ACS Nano 12(2), 1656–1663 (2018)

    Article  Google Scholar 

  23. A. Trafton, Mimicking the Brain in Silicon https://news.mit.edu/2011/brain-chip-1115

  24. V.K. Sangwan, D. Jariwala, I.S. Kim, K.S. Chen, T.J. Marks, L.J. Lauhon, M.C. Hersam, Gate-tunable memristive phenomena mediated by grain boundaries in single-layer MoS2. Nat. Nanotechnol. 10(5), 403–406 (2015)

    Article  Google Scholar 

  25. Y. Babacan, F. Kaçar, K. Gürkan, A spiking and bursting neuron circuit based on memristor. Neurocomputing 203, 86–91 (2016)

    Article  Google Scholar 

  26. R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J.M. Bower, M. Zirpe, Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23(3), 349–398 (2007)

    Article  MathSciNet  Google Scholar 

  27. Scheduling In Go: Part I—OS Scheduler, https://www.ardanlabs.com/blog/2018/08/scheduling-in-go-part1.html

  28. Brain Basics: The Life and Death of a Neuron, https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Life-and-Death-Neuron

  29. A.M. Rossi, V.M. Fernandes, C. Desplan, Timing temporal transitions during brain development. Curr. Opin. Neurobiol. 42, 84–92 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. PG & Research Department of Computer Science, Dr. Ambedkar Government Arts College (Affiliated to University of Madras), Vyasarpadi, Chennai, 600039, India

    R. Krishnan & A. Murugan

Authors
  1. R. Krishnan
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  2. A. Murugan
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Departamento de Engenharia Mecanica, SDI, Faculty of Engineering, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

  4. College of Graduate Studies, University of Central Florida, Orlando, FL, USA

    Fuqian Shi

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Krishnan, R., Murugan, A. (2021). Object-Based Neural Model in Multicore Environments with Improved Biological Plausibility. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_2

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