江苏龙网

  1. 主页 > 生活百科 > >

大脑如何储存长期记忆、检索回忆、做决策？这些大脑的运行机制值得AI学习( 四 )

AI


未完待续......
参考文献：

  • 1. Luyao Chen, Z.C., Longsheng Jiang, Xiang Liu, Linlu Xu, Bo Zhang, Xiaolong Zou, Jinying Gao, Yu Zhu, Xizi Gong, Shan Yu, Sen Song, Liangyi Chen, Fang Fang, Si Wu, Jia Liu, AI of Brain and Cognitive Sciences: From the Perspective of First Principles. arXiv, 2023. 2301.08382.
  • 2. Lim, S. and M.S. Goldman, Balanced cortical microcircuitry for maintaining information in working memory. Nat Neurosci, 2013. 16(9): p. 1306-14.
  • 3. Blumenfeld, B., et al., Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity. Neuron, 2006. 52(2): p. 383-94.
  • 4. Liu, X., et al., Neural feedback facilitates rough-to-fine information retrieval. Neural Netw, 2022. 151: p. 349-364.
  • 5. Xingsi Dong, T.C., Tiejun Huang, Zilong Ji, Si Wu, Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks. Advances in Neural Information Processing Systems, 2021. Dec 6;34:16791-804.
  • 6. Zhang, W.H., et al., Decentralized Multisensory Information Integration in Neural Systems. J Neurosci, 2016. 36(2): p. 532-47.
  • 7. Zhang, W.H., et al., Complementary congruent and opposite neurons achieve concurrent multisensory integration and segregation. Elife, 2019. 8.
  • 8. Mashour, G.A., et al., Conscious Processing and the Global Neuronal Workspace Hypothesis. Neuron, 2020. 105(5): p. 776-798.
  • 9. Mcmillan, W.L., Monte-Carlo Simulation of the Two-Dimensional Random (+/-J) Ising-Model. Physical Review B, 1983. 28(9): p. 5216-5220.
  • 10. Clauset, A., C.R. Shalizi, and M.E.J. Newman, Power-Law Distributions in Empirical Data. Siam Review, 2009. 51(4): p. 661-703.
  • 11. Beggs, J.M. and D. Plenz, Neuronal avalanches in neocortical circuits. Journal of Neuroscience, 2003. 23(35): p. 11167-11177.
  • 12. Shew, W.L., et al., Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality. Journal of Neuroscience, 2009. 29(49): p. 15595-15600.
  • 13. Zeng, G.X., et al., Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks. Neural Networks, 2019. 118: p. 140-147.
  • 14. Poole, B., et al., Exponential expressivity in deep neural networks through transient chaos. Advances in Neural Information Processing Systems 29 (Nips 2016), 2016. 29.




推荐阅读


上一篇：超越NumPy和Pandas：三个鲜为人知的Python库

下一篇：混元大模型终于亮相，全链路自研降低大模型幻觉，腾讯慢工出细活