神经科学如何影响人工智能?看DeepMind在NeurIPS2020最新《神经科学人工智能》报告

2020
12/25

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大脑仍然是唯一已知的真正通用智能系统的例子。

来源:专知




Jane Wang 是 DeepMind 神经科学团队的一名研究科学家,研究元强化学习和受神经科学启发的人工智能代理。她的背景是物理、复杂系统、计算和认知神经科学。


Kevin Miller 是 DeepMind 神经科学团队的研究科学家,也是伦敦大学学院的博士后。他目前正在研究如何理解 mice 和机器的结构化强化学习。

Adam Marblestone 是施密特期货创新公司 (Schmidt Futures innovation) 的研究员,曾是 DeepMind 的研究科学家,此前他获得了生物物理学博士学位,并在一家脑机接口公司工作。


Where Neuroscience Meets AI


地址:
https://sites.google.com/view/neurips-2020-tutorial-neurosci/home

大脑仍然是唯一已知的真正通用智能系统的例子。对人类和动物认知的研究已经揭晓了一些关键的见解,如并行分布式处理、生物视觉和从奖赏信号中学习的想法,这些都极大影响了人工学习系统的设计。许多人工智能研究人员继续将神经科学视为灵感和洞察力的来源。一个关键的困难是,神经科学是一个广泛的、异质的研究领域,包括一系列令人困惑的子领域。在本教程中,我们将从整体上对神经科学进行广泛的概述,同时重点关注两个领域——计算认知神经科学和电路学习的神经科学——我们认为这两个领域对今天的人工智能研究人员尤其相关。最后,我们将强调几项正在进行的工作,这些工作试图将神经科学领域的见解引入人工智能,反之亦然。


概要:
  1. 概述 Introduction / background (15 min)
  2. 认知神经科学 Cognitive neuroscience (30 min)
  3. 学习电路与机制神经科学, Learning circuits and mechanistic neuroscience (30 min)
  4. 交叉最新进展 Recent advancements at the intersection (25 min)

https://sites.google.com/view/neurips-2020-tutorial-neurosci/home




参考文献:

Section 1 - Cognitive Neuroscience

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Reviews: Planning

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  • Miller, K. J., Botvinick, M. M., & Brody, C. D. (2017). Dorsal hippocampus contributes to model-based planning. Nature Neuroscience.

  • Milner, A. D., Perrett, D. I., Johnston, R. S., Benson, P. J., Jordan, T. R., Heeley, D. W., Bettucci, D., Mortara, F., Mutani, R., & Terazzi, E. (1991). Perception and action in “visual form agnosia.” Brain.

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  • Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D. (2019). High-dimensional geometry of population responses in visual cortex. Nature.

  • Szwed, M., Dehaene, S., Kleinschmidt, A., Eger, E., Valabrègue, R., Amadon, A., & Cohen, L. (2011). Specialization for written words over objects in the visual cortex. NeuroImage.

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Section 2 - Circuits and Mechanistic Neuroscience


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  • O'Reilly, R.C., Hazy, T.E., Mollick, J., Mackie, P. and Herd, S., 2014. Goal-driven cognition in the brain: a computational framework. arXiv preprint arXiv:1404.7591.

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  • Körding, K.P. and König, P., 2001. Supervised and unsupervised learning with two sites of synaptic integration. Journal of computational neuroscience, 11(3), pp.207-215.

  • Whittington, J.C. and Bogacz, R., 2019. Theories of error back-propagation in the brain. Trends in cognitive sciences, 23(3), pp.235-250.

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  • Heeger, D.J., 2017. Theory of cortical function. Proceedings of the National Academy of Sciences, 114(8), pp.1773-1782.

  • Rolls, E.T., 1996. A theory of hippocampal function in memory. Hippocampus, 6(6), pp.601-620.

  • Pfeiffer, B.E. and Foster, D.J., 2015. Autoassociative dynamics in the generation of sequences of hippocampal place cells. Science, 349(6244), pp.180-183.

  • Carrillo-Reid, L., Yang, W., Bando, Y., Peterka, D.S. and Yuste, R., 2016. Imprinting and recalling cortical ensembles. Science, 353(6300), pp.691-694.

  • Müller, M.G., Papadimitriou, C.H., Maass, W. and Legenstein, R., 2020. A model for structured information representation in neural networks of the brain. Eneuro, 7(3).

  • Abbott, L.F., Bock, D.D., Callaway, E.M., Denk, W., Dulac, C., Fairhall, A.L., Fiete, I., Harris, K.M., Helmstaedter, M., Jain, V. and Kasthuri, N., 2020. The Mind of a Mouse. Cell, 182(6), pp.1372-1376.

  • Turner, N.L., Macrina, T., Bae, J.A., Yang, R., Wilson, A.M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A.L. and Bleckert, A.A., 2020. Multiscale and multimodal reconstruction of cortical structure and function. bioRxiv.

  • George D, Lehrach W, Kansky K, Lázaro-Gredilla M, Laan C, Marthi B, Lou X, Meng Z, Liu Y, Wang H, Lavin A. A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs. Science. 2017 Dec 8;358(6368).


 

Section 3 - Recent advancements at the intersection


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  • Yamins, D. L., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature neuroscience, 19(3), 356-365.

  • Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503(7474), 78-84.

  • Song, H. F., Yang, G. R., & Wang, X. J. (2017). Reward-based training of recurrent neural networks for cognitive and value-based tasks. Elife, 6, e21492.

  • Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X. J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature neuroscience, 22(2), 297-306.

  • Dezfouli, A., Morris, R., Ramos, F. T., Dayan, P., & Balleine, B. (2018). Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. In Advances in Neural Information Processing Systems (pp. 4228-4237).

  • Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., ... & Botvinick, M. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience, 21(6), 860-868.

  • Dabney, W., Kurth-Nelson, Z., Uchida, N., Starkweather, C. K., Hassabis, D., Munos, R., & Botvinick, M. (2020). A distributional code for value in dopamine-based reinforcement learning. Nature, 577(7792), 671-675.

  • Akrout, M., Wilson, C., Humphreys, P., Lillicrap, T., & Tweed, D. B. (2019). Deep learning without weight transport. In Advances in neural information processing systems (pp. 976-984).

  • Miconi, T. (2017). Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks. Elife, 6, e20899.

  • Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., & Maass, W. (2018). Long short-term memory and learning-to-learn in networks of spiking neurons. In Advances in Neural Information Processing Systems (pp. 787-797).

  • Merel, J., Aldarondo, D., Marshall, J., Tassa, Y., Wayne, G., & Ölveczky, B. (2019). Deep neuroethology of a virtual rodent. In International Conference on Learning Representations.

  • Greydanus, S., Koul, A., Dodge, J., & Fern, A. (2018, July). Visualizing and understanding atari agents. In International Conference on Machine Learning (pp. 1792-1801). PMLR.

  • Barrett, D. G., Morcos, A. S., & Macke, J. H. (2019). Analyzing biological and artificial neural networks: challenges with opportunities for synergy?. Current opinion in neurobiology, 55, 55-64.

  • Morcos, A. S., Barrett, D. G., Rabinowitz, N. C., & Botvinick, M. (2018). On the importance of single directions for generalization. In International Conference on Learning Representations.

  • Raghu, M., Gilmer, J., Yosinski, J., & Sohl-Dickstein, J. (2017). Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability. In Advances in Neural Information Processing Systems (pp. 6076-6085).

  • Puri, N., Verma, S., Gupta, P., Kayastha, D., Deshmukh, S., Krishnamurthy, B., & Singh, S. (2019, September). Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution. In International Conference on Learning Representations.

  • Sussillo, D., & Barak, O. (2013). Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural computation, 25(3), 626-649.

  • Maheswaranathan, N., Williams, A., Golub, M., Ganguli, S., & Sussillo, D. (2019). Universality and individuality in neural dynamics across large populations of recurrent networks. In Advances in neural information processing systems (pp. 15629-15641).



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关键词:
Nature,神经科学,人工智能,影响

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