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Neuroscience and Artificial Intelligence

Yıl 2022, Cilt: 2 Sayı: 1, 8 - 12, 26.04.2022

Öz

The development of artificial intelligence began with the transfer of the functioning of the nervous system to a mathematical system. Every day, studies in the field of neuroscience, which is the science that examines memory, cognitive functions and learning functions in the human brain, reveal new discoveries. The most important supporter of these studies is again technology. The nervous system has been better understood with technology-supported neuroscience studies since the beginning of the twenty-first century all over the world. In this way, developments in arti-ficial intelligence have also accelerated.

Kaynakça

  • 1. Russell S, and Norvig P, Artificial intelligence: a modern appro-ach, 4th ed., Pearson, 2002. p. 32-60.
  • 2. Turing AM, Computing machinery and intelligence, in Parsing the turing test. Springer. 2009. p. 23-65.
  • 3. Hochreiter S, and Schmidhuber J, Long Short-Term Me-mory. Neural Computation, p. 1735-1780. 1997. doi:10.1162/neco.1997.9.8.1735
  • 4. Hinton GE, et al. “Distributed Representations.” The Philosop-hy of Artificial Intelligence 1990.
  • 5. Morris RG, Hebb DO, The Organization of Behavior, Vol. 65. Wiley: New York; 1949. doi:10.1016/s0361-9230(99)00182-3
  • 6. Rosenblatt F, The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 1958. p. 386. https://doi.org/10.1037/h0042519
  • 7. Hubel DH, Wiesel TN, Receptive fields of single neurones in the cat’s striate cortex, Brain Physiology and Psychology. Uni-versity of California Press. 2020. p. 129-150. doi:10.1113/jphy-siol.1959.sp006308
  • 8. LeCun Y, et al., Backpropagation applied to handwritten zip code recognition. Neural computation, 1989. 1(4): p. 541-551.
  • 9. Krizhevsky A, Sutskever I, Hinton GE, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25: p. 1097-1105. doi: 10.1145/3065386
  • 10. LeCun Y, Bengio Y, Hinton G, Deep learning. nature, 2015. 521(7553): p. 436-444.
  • 11. Rumelhart DE, Hinton GE, Williams RJ, Learning internal representations by error propagation, California Univ San Diego La Jolla Inst for Cognitive Science. 1986. https://doi.or-g/10.1038/323533a0
  • 12. James W, The principles of psychology. Vol. 1. Cosimo, Inc. 2007. p. 40-72.
  • 13. Raichle ME, Positron emission tomography. Annual review of neuroscience, 1983. 6(1): p. 249-267.
  • 14. Scolari M, Seidl-Rathkopf KN, Kastner S, Functions of the hu-man frontoparietal attention network: Evidence from neuroima-ging. Current opinion in behavioral sciences, 2015. 1: p. 32-39.
  • 15. Bahdanau D, Cho K, Bengio Y, Neural machine translati-on by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
  • 16. Hassabis D, et al., Neuroscience-Inspired Artificial Intel-ligence. Neuron, 2017. 95(2): p. 245-258. doi:10.1016/j.neuron.2017.06.011
  • 17. Marr D, and Poggio T, From understanding computation to un-derstanding neural circuitry. 1976.
  • 18. Graves A, et al., Hybrid computing using a neural network with dynamic external memory. Nature, 2016. 538(7626): p. 471-476. https://doi.org./10.1038/nature20101
  • 19. Santoro A, et al., One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065, 2016.20. Legg S, Hutter M, A collection of definitions of intelligence. Frontiers in Artificial Intelligence and applications, 2007. 157: p. 17.

Sinir Bilim ve Yapay Zekâ

Yıl 2022, Cilt: 2 Sayı: 1, 8 - 12, 26.04.2022

Öz

Yapay zekâ gelişimi, sinir sisteminin işleyişinin matematiksel bir sisteme aktarılması ile başlamıştır. İnsan beynindeki hafıza, bilişsel işlevler ve öğrenme fonksiyonlarını inceleyen bilim dalı olan sinir bilimleri alanın-da her geçen gün çalışmalar yeni keşifler ortaya koymaktadır. Bu çalış-maların en önemli destekçisi yine teknolojidir. Tüm dünyada yirmi birin-ci yüzyıl başlarından itibaren teknoloji destekli sinir bilim çalışmaları ile sinir sistemi daha iyi anlaşılmıştır. Bu sayede yapay zekâdaki gelişmeler de hız kazanmıştır.

Kaynakça

  • 1. Russell S, and Norvig P, Artificial intelligence: a modern appro-ach, 4th ed., Pearson, 2002. p. 32-60.
  • 2. Turing AM, Computing machinery and intelligence, in Parsing the turing test. Springer. 2009. p. 23-65.
  • 3. Hochreiter S, and Schmidhuber J, Long Short-Term Me-mory. Neural Computation, p. 1735-1780. 1997. doi:10.1162/neco.1997.9.8.1735
  • 4. Hinton GE, et al. “Distributed Representations.” The Philosop-hy of Artificial Intelligence 1990.
  • 5. Morris RG, Hebb DO, The Organization of Behavior, Vol. 65. Wiley: New York; 1949. doi:10.1016/s0361-9230(99)00182-3
  • 6. Rosenblatt F, The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 1958. p. 386. https://doi.org/10.1037/h0042519
  • 7. Hubel DH, Wiesel TN, Receptive fields of single neurones in the cat’s striate cortex, Brain Physiology and Psychology. Uni-versity of California Press. 2020. p. 129-150. doi:10.1113/jphy-siol.1959.sp006308
  • 8. LeCun Y, et al., Backpropagation applied to handwritten zip code recognition. Neural computation, 1989. 1(4): p. 541-551.
  • 9. Krizhevsky A, Sutskever I, Hinton GE, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25: p. 1097-1105. doi: 10.1145/3065386
  • 10. LeCun Y, Bengio Y, Hinton G, Deep learning. nature, 2015. 521(7553): p. 436-444.
  • 11. Rumelhart DE, Hinton GE, Williams RJ, Learning internal representations by error propagation, California Univ San Diego La Jolla Inst for Cognitive Science. 1986. https://doi.or-g/10.1038/323533a0
  • 12. James W, The principles of psychology. Vol. 1. Cosimo, Inc. 2007. p. 40-72.
  • 13. Raichle ME, Positron emission tomography. Annual review of neuroscience, 1983. 6(1): p. 249-267.
  • 14. Scolari M, Seidl-Rathkopf KN, Kastner S, Functions of the hu-man frontoparietal attention network: Evidence from neuroima-ging. Current opinion in behavioral sciences, 2015. 1: p. 32-39.
  • 15. Bahdanau D, Cho K, Bengio Y, Neural machine translati-on by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
  • 16. Hassabis D, et al., Neuroscience-Inspired Artificial Intel-ligence. Neuron, 2017. 95(2): p. 245-258. doi:10.1016/j.neuron.2017.06.011
  • 17. Marr D, and Poggio T, From understanding computation to un-derstanding neural circuitry. 1976.
  • 18. Graves A, et al., Hybrid computing using a neural network with dynamic external memory. Nature, 2016. 538(7626): p. 471-476. https://doi.org./10.1038/nature20101
  • 19. Santoro A, et al., One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065, 2016.20. Legg S, Hutter M, A collection of definitions of intelligence. Frontiers in Artificial Intelligence and applications, 2007. 157: p. 17.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka (Diğer)
Bölüm Derlemeler
Yazarlar

Sema Gül Türk

Murat Terzi 0000-0002-3586-9115

Yayımlanma Tarihi 26 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 2 Sayı: 1

Kaynak Göster

Vancouver Gül Türk S, Terzi M. Sinir Bilim ve Yapay Zekâ. JAIHS. 2022;2(1):8-12.