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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

2020-12-11 11:59| 发布者: 炼数成金_小数| 查看: 73443| 评论: 0|来自: 专知

摘要: 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 ...

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)
交叉进展 Recent advancements at the intersection (25 min)

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



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Section 2 - Circuits and Mechanistic Neuroscience
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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).

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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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