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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 年 12 月 11 日 学术头条

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：

概述 Introduction / background (15 min)
认知神经科学 Cognitive neuroscience (30 min)
学习电路与机制神经科学， Learning circuits and mechanistic neuroscience (30 min)
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

Textbooks

Gazzaniga, M., Ivry, R. B., & Mangun, G. R. (2018). Cognitive Neuroscience. W.W. Norton & Company.
O’Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience. MIT Press.
Abbot, L. F., & Dayan, P. (2001). Theoretical neuroscience. MIT Press.

Reviews: Innateness

Zador, A. M. (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications.
Pinker, S. (2003). The Language Instinct: How the Mind Creates Language. Penguin UK.
Hinton, G. E., & Nowlan, S. J. (1987). How learning can guide evolution. Complex Systems

Reviews: Vision

Goodale, M. A., & Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neurosciences,
Andersen, R. A., & Buneo, C. A. (2002). Intentional Maps in Posterior Parietal Cortex. Annual Review of Neuroscience .
Whitwell, R. L., Milner, A. D., & Goodale, M. A. (2014). The Two Visual Systems Hypothesis: New Challenges and Insights from Visual form Agnosic Patient DF. Frontiers in Neurology.
DiCarlo, J. J., & Cox, D. D. (2007). Untangling invariant object recognition. Trends in Cognitive Sciences.
Dehaene, S., & Cohen, L. (2011). The unique role of the visual word form area in reading. Trends in Cognitive Sciences.
Kanwisher, N., & Yovel, G. (2006). The fusiform face area: a cortical region specialized for the perception of faces. Phil. Trans. Royal Society of London: B.

Reviews: Memory

Squire, L. R. (2009). The legacy of patient HM for neuroscience. Neuron.
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review.
O’Reilly, R. C., & Norman, K. A. (2002). Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework. Trends in Cognitive Sciences.

Reviews: Planning

Unterrainer, J. M., & Owen, A. M. (2006). Planning and problem solving: from neuropsychology to functional neuroimaging. Journal of Physiology.
Dolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron
Miller, K. J., & Venditto, S. J. C. (2021). Multi-step planning in the brain. Current Opinion in Behavioral Sciences

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Section 2 - Circuits and Mechanistic Neuroscience

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Gielow, M.R. and Zaborszky, L., 2017. The input-output relationship of the cholinergic basal forebrain. Cell reports, 18(7), pp.1817-1830.
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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.
O'Reilly, R.C., Wyatte, D. and Rohrlich, J., 2014. Learning through time in the thalamocortical loops. arXiv preprint arXiv:1407.3432.
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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.
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Section 3 - Recent advancements at the intersection

Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619-8624.
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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).