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Deep Reinforcement Learning and Its Neuroscientific Implications.
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If deep learning is the answer, what is the question?
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Discovering cognitive strategies with tiny recurrent neural networks.
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Modeling heterogeneity, commitment, and memory of bacterial spore germination.
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本文引用的文献

2
Deep Reinforcement Learning and Its Neuroscientific Implications.
Neuron. 2020 Aug 19;107(4):603-616. doi: 10.1016/j.neuron.2020.06.014. Epub 2020 Jul 13.
3
Attention in Psychology, Neuroscience, and Machine Learning.
Front Comput Neurosci. 2020 Apr 16;14:29. doi: 10.3389/fncom.2020.00029. eCollection 2020.
4
Backpropagation and the brain.
Nat Rev Neurosci. 2020 Jun;21(6):335-346. doi: 10.1038/s41583-020-0277-3. Epub 2020 Apr 17.
5
Continual Learning Through Synaptic Intelligence.
Proc Mach Learn Res. 2017;70:3987-3995.
6
A deep learning framework for neuroscience.
Nat Neurosci. 2019 Nov;22(11):1761-1770. doi: 10.1038/s41593-019-0520-2. Epub 2019 Oct 28.
7
Recurrence is required to capture the representational dynamics of the human visual system.
Proc Natl Acad Sci U S A. 2019 Oct 22;116(43):21854-21863. doi: 10.1073/pnas.1905544116. Epub 2019 Oct 7.
8
Towards artificial general intelligence with hybrid Tianjic chip architecture.
Nature. 2019 Aug;572(7767):106-111. doi: 10.1038/s41586-019-1424-8. Epub 2019 Jul 31.
9
Using DeepLabCut for 3D markerless pose estimation across species and behaviors.
Nat Protoc. 2019 Jul;14(7):2152-2176. doi: 10.1038/s41596-019-0176-0. Epub 2019 Jun 21.
10
Circuit mechanisms for the maintenance and manipulation of information in working memory.
Nat Neurosci. 2019 Jul;22(7):1159-1167. doi: 10.1038/s41593-019-0414-3. Epub 2019 Jun 10.

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