聯結主義
聯結主義(粵拼:lyun4 git3 zyu2 ji6;英文:connectionism)係喺廿世紀中後期興起嘅一種認知科學(cognitive science)觀點,主張心靈可以概念化想像成人工神經網絡(artificial neural network)模型[1][2]。
人工神經網絡泛指模擬生物神經網絡(biological neural network)嘅數學模型:一隻(例如)靈長目動物嘅腦閒閒地有斷百億計嘅神經細胞(neuron),一粒神經細胞喺俾電同化學物訊號刺激到嗰陣,會跟住以電或者化學物嚟傳新訊號,所以當一粒神經細胞射訊號嗰陣可以引起連鎖反應,將資訊喺成個神經網絡嗰度傳開去[3][4]。一個人工神經網絡由大量嘅人工神經細胞組成。喺用電腦程式整神經網絡嗰陣,研究者會每粒人工神經細胞同佢設返條類似噉嘅式[5][6]:
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喺呢條式當中, 代表嗰粒神經細胞嘅啟動程度, 代表其他神經細胞當中第 粒嘅啟動程度,而 就係其他神經細胞當中第 粒嘅權重(指嗰粒神經細胞有幾影響到 )。所以當一粒人工神經細胞啟動嗰陣,會帶起佢後面啲人工神經細胞跟住佢啟動-似十足生物神經網絡入面嗰啲神經細胞噉。假如個神經網絡嘅程式令佢能夠自行按經驗改變 嘅數值嘅話,佢就會曉學習[5][7]。
截至廿一世紀初,聯結主義經已取得咗相當嘅成功:人工神經網絡建基於現實世界嘅心靈-人腦,所以具有結構上嘅可信性,而且人工神經網絡仲有遞迴神經網絡同卷積神經網絡等多個變種;喺好多情況下,人工神經網絡都能夠俾到同現實觀察極之相近嘅輸入輸出關係;因為呢啲緣故,有唔少認知科學家認為,人工神經網絡可以攞嚟做心靈嘅模型,解釋心靈相關嘅現象[8]。
記憶
編輯瞓覺
編輯廿一世紀初嘅研究顯示,人工神經網絡可以用嚟模擬瞓覺嘅現象:
睇埋
編輯參考文獻
編輯- Rumelhart, D.E., J.L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, Cambridge, Massachusetts: MIT Press, ISBN 978-0262680530
- McClelland, J.L., D.E. Rumelhart and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models, Cambridge, Massachusetts: MIT Press, ISBN 978-0262631105
- Pinker, Steven and Mehler, Jacques (1988). Connections and Symbols, Cambridge MA: MIT Press, ISBN 978-0262660648
- Jeffrey L. Elman, Elizabeth A. Bates, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi, Kim Plunkett (1996). Rethinking Innateness: A connectionist perspective on development, Cambridge MA: MIT Press, ISBN 978-0262550307
- Krishnan, G. P., Tadros, T., Ramyaa, R., & Bazhenov, M. (2019). Biologically inspired sleep algorithm for artificial neural networks (PDF). arXiv preprint arXiv:1908.02240.
- Marcus, Gary F. (2001). The Algebraic Mind: Integrating Connectionism and Cognitive Science (Learning, Development, and Conceptual Change), Cambridge, Massachusetts: MIT Press, ISBN 978-0262632683
- David A. Medler (1998). "A Brief History of Connectionism" (PDF). Neural Computing Surveys. 1: 61–101.
攷
編輯- ↑ Smolensky, Paul (1999). "Grammar-based Connectionist Approaches to Language 互聯網檔案館嘅歸檔,歸檔日期2020年9月22號,." (PDF). Cognitive Science. 23 (4): 589–613.
- ↑ Marcus, Gary F. (2001). The Algebraic Mind: Integrating Connectionism and Cognitive Science (Learning, Development, and Conceptual Change). Cambridge, Massachusetts: MIT Press. pp. 27–28.
- ↑ Russell, Stuart J.; Norvig, Peter (2010). Artificial Intelligence A Modern Approach. Prentice Hall. p. 578.
- ↑ Omidvar, O., & Elliott, D. L. (1997). Neural systems for control. Elsevier.
- ↑ 5.0 5.1 Learning process of a neural network 互聯網檔案館嘅歸檔,歸檔日期2021年2月11號,.. Towards Data Science.
- ↑ Bryson, Arthur Earl (1969). Applied Optimal Control: Optimization, Estimation and Control. Blaisdell Publishing Company or Xerox College Publishing. p. 481.
- ↑ The Machine Learning Dictionary - activation level 互聯網檔案館嘅歸檔,歸檔日期2018年8月26號,..
- ↑ The Actual Difference Between Statistics and Machine Learning. Towards Data Science.
- ↑ Robert Stickgold and Matthew P Walker. Sleep-dependent memory triage: evolving generalization through selective processing. Nature neuroscience, 16(2):139, (2013).
- ↑ Björn Rasch and Jan Born. About sleep's role in memory. Physiological reviews, 93(2):681–766, (2013).
- ↑ McCloskey, M. & Cohen, N. (1989). Catastrophic interference in connectionist networks: The sequential learning problem (PDF). In G. H. Bower (ed.) The Psychology of Learning and Motivation, 24, 109-164
- ↑ Researchers Made an AI Whose Performance Increases if They Let It Sleep And Dream. ScienceAlert.
- ↑ Krishnan, G. P., Tadros, T., Ramyaa, R., & Bazhenov, M. (2019). Biologically inspired sleep algorithm for artificial neural networks. arXiv preprint arXiv:1908.02240.