{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:52:30Z","timestamp":1793314350844,"version":"4.5.0"},"reference-count":178,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2017,7,1]],"date-time":"2017-07-01T00:00:00Z","timestamp":1498867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T00:00:00Z","timestamp":1531958400000},"content-version":"vor","delay-in-days":383,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neuron"],"published-print":{"date-parts":[[2017,7]]},"DOI":"10.1016\/j.neuron.2017.06.011","type":"journal-article","created":{"date-parts":[[2017,7,19]],"date-time":"2017-07-19T14:33:04Z","timestamp":1500474784000},"page":"245-258","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1337,"title":["Neuroscience-Inspired Artificial Intelligence"],"prefix":"10.1016","volume":"95","author":[{"given":"Demis","family":"Hassabis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dharshan","family":"Kumaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Summerfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Botvinick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neuron.2017.06.011_bib1","series-title":"Action as an organizer of perception and cognition during learning and development","first-page":"91","article-title":"Learning to learn in the development of action","author":"Adolph","year":"2005"},{"key":"10.1016\/j.neuron.2017.06.011_bib2","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1037\/0033-295X.111.4.1036","article-title":"An integrated theory of the mind","volume":"111","author":"Anderson","year":"2004","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib3","unstructured":"Ba, J.L., Mnih, V., and Kavukcuoglu, K. (2015). Multiple object recognition with visual attention. arXiv, arXiv:14127755."},{"key":"10.1016\/j.neuron.2017.06.011_bib4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1146\/annurev-psych-120710-100422","article-title":"Working memory: theories, models, and controversies","volume":"63","author":"Baddeley","year":"2012","journal-title":"Annu. Rev. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib5","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv, arXiv:14090473."},{"key":"10.1016\/j.neuron.2017.06.011_bib6","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.neuron.2016.03.037","article-title":"Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network","volume":"90","author":"Balaguer","year":"2016","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib7","series-title":"The Mechanisation of Thought Processes","first-page":"535","article-title":"Sensory mechanisms, the reduction of redundancy, and intelligence","author":"Barlow","year":"1959"},{"key":"10.1016\/j.neuron.2017.06.011_bib8","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1037\/0033-2909.128.4.612","article-title":"When and where do we apply what we learn? A taxonomy for far transfer","volume":"128","author":"Barnett","year":"2002","journal-title":"Psychol. Bull."},{"key":"10.1016\/j.neuron.2017.06.011_bib9","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.neuron.2012.10.038","article-title":"Canonical microcircuits for predictive coding","volume":"76","author":"Bastos","year":"2012","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib10","doi-asserted-by":"crossref","first-page":"18327","DOI":"10.1073\/pnas.1306572110","article-title":"Simulation as an engine of physical scene understanding","volume":"110","author":"Battaglia","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.neuron.2017.06.011_bib11","unstructured":"Battaglia, P., Pascanu, R., Lai, M., Rezende, D., and Kavukcuoglu, K. (2016). Interaction networks for learning about objects, relations and physics. arXiv, arXiv:161200222."},{"key":"10.1016\/j.neuron.2017.06.011_bib12","unstructured":"Bengio, Y., Lee, D.H., Bornschein, J., Mesnard, T., and Lin, Z. (2015). Towards biologically plausible deep learning. arXiv, arXiv:150204156."},{"key":"10.1016\/j.neuron.2017.06.011_bib13","doi-asserted-by":"crossref","first-page":"10464","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998","article-title":"Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type","volume":"18","author":"Bi","year":"1998","journal-title":"J.\u00a0Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib14","unstructured":"Blundell, C., Uria, B., Pritzel, A., Yazhe, L., Ruderman, A., Leibo, J.Z., Rae, J., Wierstra, D., and Hassabis, D. (2016). Model-free episodic control. arXiv, arXiv:160604460."},{"key":"10.1016\/j.neuron.2017.06.011_bib15","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1037\/0033-295X.113.2.201","article-title":"Short-term memory for serial order: a recurrent neural network model","volume":"113","author":"Botvinick","year":"2006","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib16","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/482462a","article-title":"Turing centenary: is the brain a good model for machine intelligence?","volume":"482","author":"Brooks","year":"2012","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCIAIG.2012.2186810","article-title":"A survey of Monte-Carlo tree search methods","volume":"4","author":"Browne","year":"2012","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"10.1016\/j.neuron.2017.06.011_bib18","unstructured":"Chang, M.B., Ullman, T., Torralba, A., and Tenenbaum, J.B. (2016). A compositional object-based approach to learning physical dynamics. arXiv, arXiv:161200341."},{"key":"10.1016\/j.neuron.2017.06.011_bib19","unstructured":"Chiappa, S., Racaniere, S., Wierstra, D., and Mohamed, S. (2017). Recurrent environment simulators. Proceedings of the 32nd International Conference on Machine Learning, pp. 1\u201361."},{"key":"10.1016\/j.neuron.2017.06.011_bib20","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1126\/science.3055294","article-title":"Perspectives on cognitive neuroscience","volume":"242","author":"Churchland","year":"1988","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib21","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1038\/nature14251","article-title":"Branch-specific dendritic Ca(2+) spikes cause persistent synaptic plasticity","volume":"520","author":"Cichon","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib22","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/nn.3635","article-title":"Resolving human object recognition in space and time","volume":"17","author":"Cichy","year":"2014","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib23","doi-asserted-by":"crossref","first-page":"e1001293","DOI":"10.1371\/journal.pbio.1001293","article-title":"Reasoning, learning, and creativity: frontal lobe function and human decision-making","volume":"10","author":"Collins","year":"2012","journal-title":"PLoS Biol."},{"key":"10.1016\/j.neuron.2017.06.011_bib24","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1126\/science.aaf0941","article-title":"Organizing conceptual knowledge in humans with a gridlike code","volume":"352","author":"Constantinescu","year":"2016","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib25","series-title":"The Nature of Explanation","author":"Craik","year":"1943"},{"key":"10.1016\/j.neuron.2017.06.011_bib26","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1038\/nn.3381","article-title":"Attention during natural vision warps semantic representation across the human brain","volume":"16","author":"\u00c7ukur","year":"2013","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib27","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1038\/nn1560","article-title":"Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control","volume":"8","author":"Daw","year":"2005","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib28","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.neuron.2013.10.032","article-title":"Engineering approaches to illuminating brain structure and dynamics","volume":"80","author":"Deisseroth","year":"2013","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib29","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009). Imagenet: a large-scale hierarchical image database. In Computer Vision and Pattern Recognition, pp. 1\u20138.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10.1016\/j.neuron.2017.06.011_bib30","unstructured":"Denil, M., Agrawal, P., Kulkarni, T.D., Erez, T., Battaglia, P., and de Freitas, N. (2016). Learning to perform physics experiments via deep reinforcement learning. arXiv, arXiv:161101843."},{"key":"10.1016\/j.neuron.2017.06.011_bib31","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.neuron.2013.09.007","article-title":"Goals and habits in the brain","volume":"80","author":"Dolan","year":"2013","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib32","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1038\/nn.3981","article-title":"Model-based choices involve prospective neural activity","volume":"18","author":"Doll","year":"2015","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib33","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1126\/science.1252254","article-title":"Human cognition. Foundations of human reasoning in the prefrontal cortex","volume":"344","author":"Donoso","year":"2014","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1037\/0033-295X.115.1.1","article-title":"A theory of the discovery and predication of relational concepts","volume":"115","author":"Doumas","year":"2008","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib35","unstructured":"Duan, Y., Schulman, J., Chen, X., Bartlett, P.L., Sutskever, I., and Abbeel, P. (2016). RL\u02c62: fast reinforcement learning via slow reinforcement learning. arXiv, arXiv:1611.02779."},{"issue":"Suppl","key":"10.1016\/j.neuron.2017.06.011_bib36","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1038\/81460","article-title":"Neurocomputational models of working memory","volume":"3","author":"Durstewitz","year":"2000","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib38","unstructured":"Eslami, A., Heess, N., Weber, T.Y.T., Szepesvari, D., Kavukcuoglu, K., and Hinton, G. (2016). Attend, infer, repeat: fast scene understanding with generative models. arXiv, arXiv:160308575."},{"key":"10.1016\/j.neuron.2017.06.011_bib39","doi-asserted-by":"crossref","first-page":"11441","DOI":"10.1073\/pnas.1604850113","article-title":"Convolutional networks for fast, energy-efficient neuromorphic computing","volume":"113","author":"Esser","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.neuron.2017.06.011_bib40","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0010-0277(88)90031-5","article-title":"Connectionism and cognitive architecture: a critical analysis","volume":"28","author":"Fodor","year":"1988","journal-title":"Cognition"},{"key":"10.1016\/j.neuron.2017.06.011_bib41","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","article-title":"Catastrophic forgetting in connectionist networks","volume":"3","author":"French","year":"1999","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib42","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","article-title":"Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position","volume":"36","author":"Fukushima","year":"1980","journal-title":"Biol. Cybern."},{"key":"10.1016\/j.neuron.2017.06.011_bib43","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.neuron.2005.02.001","article-title":"Cascade models of synaptically stored memories","volume":"45","author":"Fusi","year":"2005","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib44","series-title":"Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience","author":"Gallistel","year":"2009"},{"key":"10.1016\/j.neuron.2017.06.011_bib45","unstructured":"Gatys, L.A., Ecker, A.S., and Bethge, M. (2015). A neural algorithm of artistic style. arXiv, arXiv:1508.06576."},{"key":"10.1016\/j.neuron.2017.06.011_bib46","unstructured":"Gemici, M., Hung, C., Santoro, A., Wayne, G., Mohamed, S., Rezende, D., Amos, D., and Lillicrap, T. (2017). Generative temporal models with memory. arXiv, arXiv:170204649."},{"key":"10.1016\/j.neuron.2017.06.011_bib47","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1146\/annurev-psych-122414-033625","article-title":"Reinforcement learning and episodic memory in humans and animals: an integrative framework","volume":"68","author":"Gershman","year":"2017","journal-title":"Annu. Rev. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib48","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1038\/nature05850","article-title":"Symbolic arithmetic knowledge without instruction","volume":"447","author":"Gilmore","year":"2007","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib49","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1038\/nn.4361","article-title":"The Human Connectome Project\u2019s neuroimaging approach","volume":"19","author":"Glasser","year":"2016","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib50","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/S0079-6123(08)62688-6","article-title":"Cellular and circuit basis of working memory in prefrontal cortex of nonhuman primates","volume":"85","author":"Goldman-Rakic","year":"1990","journal-title":"Prog. Brain Res."},{"key":"10.1016\/j.neuron.2017.06.011_bib51","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.tics.2004.06.005","article-title":"Mechanisms of theory formation in young children","volume":"8","author":"Gopnik","year":"2004","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib52","unstructured":"Graves, A., Wayne, G., and Danihelka, I. (2014). Neural turing machines. arXiv, arXiv:1410.5401."},{"key":"10.1016\/j.neuron.2017.06.011_bib53","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1038\/nature20101","article-title":"Hybrid computing using a neural network with dynamic external memory","volume":"538","author":"Graves","year":"2016","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib54","unstructured":"Gregor, K., Danihelka, I., Graves, A., Renzende, D., and Wierstra, D. (2015). DRAW: a recurrent neural network for image generation. arXiv, arXiv:150204623."},{"key":"10.1016\/j.neuron.2017.06.011_bib55","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10994-011-5235-x","article-title":"Reinforcement learning in feedback control","volume":"84","author":"Hafner","year":"2011","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neuron.2017.06.011_bib56","unstructured":"Hamrick, J.B., Ballard, A.J., Pascanu, R., Vinyals, O., Heess, N., and Battaglia, P.W. (2017). Metacontrol for adaptive imagination-based optimization. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), pp. 1\u201321."},{"key":"10.1016\/j.neuron.2017.06.011_bib57","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1037\/h0062474","article-title":"The formation of learning sets","volume":"56","author":"Harlow","year":"1949","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib58","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.tics.2007.05.001","article-title":"Deconstructing episodic memory with construction","volume":"11","author":"Hassabis","year":"2007","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib59","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1098\/rstb.2008.0296","article-title":"The construction system of the brain","volume":"364","author":"Hassabis","year":"2009","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib60","series-title":"Artificial Intelligence: The Very Idea","author":"Haugeland","year":"1985"},{"key":"10.1016\/j.neuron.2017.06.011_bib61","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nature15257","article-title":"Labelling and optical erasure of synaptic memory traces in the motor cortex","volume":"525","author":"Hayashi-Takagi","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib62","series-title":"The Organization of Behavior","author":"Hebb","year":"1949"},{"key":"10.1016\/j.neuron.2017.06.011_bib63","unstructured":"Higgins, I., Matthey, L., Glorot, X., Pal, A., Uria, B., Blundell, C., Mohamed, S., and Lerchner, A. (2016). Early visual concept learning with unsupervised deep learning. arXiv, arXiv:160605579."},{"key":"10.1016\/j.neuron.2017.06.011_bib64","series-title":"Explorations in the Microstructure of Cognition","first-page":"77","article-title":"Distributed Representations","author":"Hinton","year":"1986"},{"key":"10.1016\/j.neuron.2017.06.011_bib65","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"10.1016\/j.neuron.2017.06.011_bib66","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv, arXiv:12070580."},{"key":"10.1016\/j.neuron.2017.06.011_bib67","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.neuron.2017.06.011_bib68","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1037\/0003-066X.52.1.35","article-title":"The analogical mind","volume":"52","author":"Holyoak","year":"1997","journal-title":"Am. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib69","unstructured":"Hong, S., Oh, J., Bohyung, H., and Lee, H. (2015). Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. arXiv, arXiv:151207928."},{"key":"10.1016\/j.neuron.2017.06.011_bib70","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1038\/nn.4247","article-title":"Explicit information for category-orthogonal object properties increases along the ventral stream","volume":"19","author":"Hong","year":"2016","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib71","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","article-title":"Neural networks and physical systems with emergent collective computational abilities","volume":"79","author":"Hopfield","year":"1982","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.neuron.2017.06.011_bib72","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1126\/science.3755256","article-title":"Computing with neural circuits: a model","volume":"233","author":"Hopfield","year":"1986","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib73","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1113\/jphysiol.1959.sp006308","article-title":"Receptive fields of single neurones in the cat\u2019s striate cortex","volume":"148","author":"Hubel","year":"1959","journal-title":"J.\u00a0Physiol."},{"key":"10.1016\/j.neuron.2017.06.011_bib74","doi-asserted-by":"crossref","first-page":"e1002410","DOI":"10.1371\/journal.pcbi.1002410","article-title":"Bonsai trees in your head: how the pavlovian system sculpts goal-directed choices by pruning decision trees","volume":"8","author":"Huys","year":"2012","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.neuron.2017.06.011_bib75","doi-asserted-by":"crossref","first-page":"12176","DOI":"10.1523\/JNEUROSCI.3761-07.2007","article-title":"Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point","volume":"27","author":"Johnson","year":"2007","journal-title":"J.\u00a0Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib76","doi-asserted-by":"crossref","first-page":"e1005268","DOI":"10.1371\/journal.pcbi.1005268","article-title":"Could a neuroscientist understand a microprocessor?","volume":"13","author":"Jonas","year":"2017","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.neuron.2017.06.011_bib77","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0166-4115(97)80111-2","article-title":"Serial order: a parallel distributed processing approach","volume":"121","author":"Jordan","year":"1997","journal-title":"Adv. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib78","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1111\/j.1551-6709.2010.01128.x","article-title":"Learning to learn causal models","volume":"34","author":"Kemp","year":"2010","journal-title":"Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib79","doi-asserted-by":"crossref","first-page":"e1003915","DOI":"10.1371\/journal.pcbi.1003915","article-title":"Deep supervised, but not unsupervised, models may explain IT cortical representation","volume":"10","author":"Khaligh-Razavi","year":"2014","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.neuron.2017.06.011_bib80","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.neuron.2017.06.011_bib81","first-page":"219","article-title":"Shifts in selective visual attention: towards the underlying neural circuitry","volume":"4","author":"Koch","year":"1985","journal-title":"Hum. Neurobiol."},{"key":"10.1016\/j.neuron.2017.06.011_bib82","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.neuron.2016.12.041","article-title":"Neuroscience needs behavior: correcting a reductionist bias","volume":"93","author":"Krakauer","year":"2017","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib83","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.tics.2013.06.007","article-title":"Representational geometry: integrating cognition, computation, and the brain","volume":"17","author":"Kriegeskorte","year":"2013","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib84","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, pp. 1097\u20131105."},{"key":"10.1016\/j.neuron.2017.06.011_bib85","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1037\/a0028681","article-title":"Generalization through the recurrent interaction of episodic memories: a model of the hippocampal system","volume":"119","author":"Kumaran","year":"2012","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib86","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.tics.2016.05.004","article-title":"What learning systems do intelligent agents need? Complementary learning systems theory updated","volume":"20","author":"Kumaran","year":"2016","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib87","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.neuron.2016.05.028","article-title":"Fast sequences of non-spatial state representations in humans","volume":"91","author":"Kurth-Nelson","year":"2016","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib88","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1126\/science.aab3050","article-title":"Human-level concept learning through probabilistic program induction","volume":"350","author":"Lake","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib89","unstructured":"Lake, B.M., Ullman, T.D., Tenenbaum, J.B., and Gershman, S.J. (2016). Building machines that learn and think like people. arXiv, arXiv:1604.00289."},{"key":"10.1016\/j.neuron.2017.06.011_bib90","unstructured":"Larochelle, H., and Hinton, G. (2010). Learning to combine foveal glimpses with a third-order Boltzmann machine. NIPS\u201910 Proceedings of the International Conference on Neural Information Processing Systems, pp. 1243\u20131251."},{"key":"10.1016\/j.neuron.2017.06.011_bib91","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"10.1016\/j.neuron.2017.06.011_bib92","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib93","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.cub.2016.10.015","article-title":"View-tolerant face recognition and Hebbian Learning imply mirror-symmetric neural tuning to head orientation","volume":"27","author":"Leibo","year":"2017","journal-title":"Curr. Biol."},{"key":"10.1016\/j.neuron.2017.06.011_bib94","series-title":"Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms\u2014Proceedings of the AGI Workshop","first-page":"17","article-title":"A collection of definitions of intelligence","author":"Legg","year":"2007"},{"key":"10.1016\/j.neuron.2017.06.011_bib95","unstructured":"Lengyel, M., and Dayan, P. (2007). Hippocampal contributions to control: the third way. In Advances in Neural Information Processing Systems 20, pp. 889\u2013896."},{"key":"10.1016\/j.neuron.2017.06.011_bib96","doi-asserted-by":"crossref","unstructured":"Liao, Q., Leibo, J.Z., and Poggio, T. (2015). How important is weight symmetry in backpropagation? arXiv, arXiv:151005067.","DOI":"10.1609\/aaai.v30i1.10279"},{"key":"10.1016\/j.neuron.2017.06.011_bib97","doi-asserted-by":"crossref","first-page":"13276","DOI":"10.1038\/ncomms13276","article-title":"Random synaptic feedback weights support error backpropagation for deep learning","volume":"7","author":"Lillicrap","year":"2016","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuron.2017.06.011_bib98","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3389\/fncom.2012.00087","article-title":"Learning to use working memory: a reinforcement learning gating model of rule acquisition in rats","volume":"6","author":"Lloyd","year":"2012","journal-title":"Front. Comput. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib99","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3389\/fncom.2016.00094","article-title":"Toward an integration of deep learning and neuroscience","volume":"10","author":"Marblestone","year":"2016","journal-title":"Front. Comput. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib100","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1006\/cogp.1998.0694","article-title":"Rethinking eliminative connectionism","volume":"37","author":"Marcus","year":"1998","journal-title":"Cognit. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib101","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/nrn1848","article-title":"The blue brain project","volume":"7","author":"Markram","year":"2006","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib102","first-page":"1","article-title":"From understanding computation to understanding neural circuitry","volume":"357","author":"Marr","year":"1976","journal-title":"A.I. Memo"},{"key":"10.1016\/j.neuron.2017.06.011_bib103","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/nrn1076","article-title":"The parallel distributed processing approach to semantic cognition","volume":"4","author":"McClelland","year":"2003","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib104","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1037\/0033-295X.102.3.419","article-title":"Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory","volume":"102","author":"McClelland","year":"1995","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib105","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of ideas immanent in nervous activity","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"10.1016\/j.neuron.2017.06.011_bib106","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014). Recurrent models of visual attention. arXiv, arXiv:14066247."},{"key":"10.1016\/j.neuron.2017.06.011_bib107","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib108","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1146\/annurev-psych-122414-033400","article-title":"Neural mechanisms of selective visual attention","volume":"68","author":"Moore","year":"2017","journal-title":"Annu. Rev. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib109","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1126\/science.aam6960","article-title":"DeepStack: expert-level artificial intelligence in heads-up no-limit poker","volume":"356","author":"Morav\u010d\u00edk","year":"2017","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib110","unstructured":"Nguyen, A., Dosovitskiy, A., Yosinski, J., Borx, T., and Clune, J. (2016). Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. arXiv, arXiv:160509304."},{"key":"10.1016\/j.neuron.2017.06.011_bib111","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.neuron.2015.05.043","article-title":"Biochemical computation for spine structural plasticity","volume":"87","author":"Nishiyama","year":"2015","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib112","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0896-6273(03)00169-7","article-title":"Temporal difference models and reward-related learning in the human brain","volume":"38","author":"O\u2019Doherty","year":"2003","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib113","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.tins.2010.01.006","article-title":"Play it again: reactivation of waking experience and memory","volume":"33","author":"O\u2019Neill","year":"2010","journal-title":"Trends Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib114","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1162\/089976606775093909","article-title":"Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia","volume":"18","author":"O\u2019Reilly","year":"2006","journal-title":"Neural Comput."},{"key":"10.1016\/j.neuron.2017.06.011_bib115","unstructured":"Oh, J., Guo, X., Lee, H., Lewis, R., and Singh, S. (2015). Action-conditional video prediction using deep networks in Atari games. arXiv, arXiv:150708750."},{"key":"10.1016\/j.neuron.2017.06.011_bib116","doi-asserted-by":"crossref","first-page":"e06063","DOI":"10.7554\/eLife.06063","article-title":"Hippocampal place cells construct reward related sequences through unexplored space","volume":"4","author":"\u00d3lafsd\u00f3ttir","year":"2015","journal-title":"eLife"},{"key":"10.1016\/j.neuron.2017.06.011_bib117","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1523\/JNEUROSCI.13-11-04700.1993","article-title":"A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information","volume":"13","author":"Olshausen","year":"1993","journal-title":"J.\u00a0Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib118","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/nature12112","article-title":"Hippocampal place-cell sequences depict future paths to remembered goals","volume":"497","author":"Pfeiffer","year":"2013","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib119","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev.ne.13.030190.000325","article-title":"The attention system of the human brain","volume":"13","author":"Posner","year":"1990","journal-title":"Annu. Rev. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib120","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1038\/nature05575","article-title":"Planning for the future by western scrub-jays","volume":"445","author":"Raby","year":"2007","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib121","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/nrn.2015.30","article-title":"Vicarious trial and error","volume":"17","author":"Redish","year":"2016","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib122","unstructured":"Reed, S., Zhang, Y., Zhang, Y., and Lee, S. (2015). Deep visual analogy-making.\u00a0In NIPS\u201915 Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 1252\u20131260."},{"key":"10.1016\/j.neuron.2017.06.011_bib123","unstructured":"Reed, S., Akata, Z., Mohan, S., Tenka, S., Schiele, B., and Lee, H. (2016). Learning what and where to draw. arXiv, arXiv:161002454."},{"key":"10.1016\/j.neuron.2017.06.011_bib124","unstructured":"Rezende, D., Eslami, A., Mohamed, S., Battaglia, P., Jaderberg, M., and Heess, N. (2016a). Unsupervised learning of 3D structure from images. arXiv, arXiv:160700662."},{"key":"10.1016\/j.neuron.2017.06.011_bib125","unstructured":"Rezende, D., Mohamed, S., Danihelka, I., Gregor, K., and Wierstra, D. (2016b). One-shot generalization in deep generative models. arXiv, arXiv:160305106."},{"key":"10.1016\/j.neuron.2017.06.011_bib126","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1038\/14819","article-title":"Hierarchical models of object recognition in cortex","volume":"2","author":"Riesenhuber","year":"1999","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib127","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib128","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1146\/annurev-neuro-070815-013824","article-title":"Ten years of grid cells","volume":"39","author":"Rowland","year":"2016","journal-title":"Annu. Rev. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib129","first-page":"318","article-title":"Learning internal representations by error propagation","volume":"Volume 1","author":"Rumelhart","year":"1985"},{"key":"10.1016\/j.neuron.2017.06.011_bib130","volume":"Volume 1","author":"Rumelhart","year":"1986"},{"key":"10.1016\/j.neuron.2017.06.011_bib131","unstructured":"Rusu, A.A., Rabinowitz, N., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016a). Progressive neural networks. arXiv, arXiv:160604671."},{"key":"10.1016\/j.neuron.2017.06.011_bib132","unstructured":"Rusu, A.A., Vecerik, M., Rothorl, T., Heess, N., Pascanu, R., and Hadsell, R. (2016b). Sim-to-real robot learning from pixels with progressive nets. arXiv, arXiv:161004286."},{"key":"10.1016\/j.neuron.2017.06.011_bib133","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1152\/jn.1997.77.6.3267","article-title":"Invariant visual responses from attentional gain fields","volume":"77","author":"Salinas","year":"1997","journal-title":"J.\u00a0Neurophysiol."},{"key":"10.1016\/j.neuron.2017.06.011_bib134","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016). One-shot Learning with Memory-Augmented Neural Networks. arXiv, arXiv:160506065."},{"key":"10.1016\/j.neuron.2017.06.011_bib177","unstructured":"Santoro, A., Raposo, D., Barrett, D.G.T., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap. T. (2017). A simple neural network module for relational reasoning. arXiv, arXiv:1706.01427, https:\/\/arxiv.org\/abs\/1706.01427."},{"key":"10.1016\/j.neuron.2017.06.011_bib135","unstructured":"Saxe, A.M., Ganguli, S., and McClelland, J.L. (2013). Exact solutions to the\u00a0nonlinear dynamics of learning in deep linear neural networks. arXiv, arXiv:13126120v3."},{"key":"10.1016\/j.neuron.2017.06.011_bib136","unstructured":"Scellier, B., and Bengio, Y. (2016). Equilibrium propagation: bridging the gap between energy-based models and backpropagation. arXiv, arXiv:160205179."},{"key":"10.1016\/j.neuron.2017.06.011_bib137","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.neuron.2012.11.001","article-title":"The future of memory: remembering, imagining, and the brain","volume":"76","author":"Schacter","year":"2012","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib138","unstructured":"Schaul, T., Quan, J., Antonoglou, I., and Silver, D. (2015). Prioritized experience replay. bioRxiv, arXiv:1511.05952."},{"key":"10.1016\/j.neuron.2017.06.011_bib139","first-page":"14047828","article-title":"Deep learning in neural networks: an overview","author":"Schmidhuber","year":"2014","journal-title":"arXiv"},{"key":"10.1016\/j.neuron.2017.06.011_bib140","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1126\/science.275.5306.1593","article-title":"A neural substrate of prediction and reward","volume":"275","author":"Schultz","year":"1997","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib141","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/TPAMI.2007.56","article-title":"Robust object recognition with cortex-like mechanisms","volume":"29","author":"Serre","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neuron.2017.06.011_bib142","series-title":"From Neuropsychology to Mental Structure","author":"Shallice","year":"1988"},{"key":"10.1016\/j.neuron.2017.06.011_bib143","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib144","unstructured":"Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv, arXiv:13126034."},{"key":"10.1016\/j.neuron.2017.06.011_bib145","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.neuron.2009.11.016","article-title":"Rewarded outcomes enhance reactivation of experience in the hippocampus","volume":"64","author":"Singer","year":"2009","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib146","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1126\/science.271.5257.1870","article-title":"Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience","volume":"271","author":"Skaggs","year":"1996","journal-title":"Science"},{"key":"10.1016\/j.neuron.2017.06.011_bib147","series-title":"Basic and Applied Perspectives on Learning, Cognition, and Development. The Minnesota Symposia on Child Psychology","first-page":"1","article-title":"Self-organizing processes in learning to learn words: development is not induction","author":"Smith","year":"1995"},{"key":"10.1016\/j.neuron.2017.06.011_bib148","doi-asserted-by":"crossref","first-page":"e1003779","DOI":"10.1371\/journal.pcbi.1003779","article-title":"Optimal behavioral hierarchy","volume":"10","author":"Solway","year":"2014","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.neuron.2017.06.011_bib149","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1111\/j.1467-7687.2007.00569.x","article-title":"Core knowledge","volume":"10","author":"Spelke","year":"2007","journal-title":"Dev. Sci."},{"key":"10.1016\/j.neuron.2017.06.011_bib150","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1146\/annurev.neuro.27.070203.144130","article-title":"The medial temporal lobe","volume":"27","author":"Squire","year":"2004","journal-title":"Annu. Rev. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib151","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/0004-3702(90)90008-N","article-title":"Learning and applying contextual constraints in sentence comprehension","volume":"46","author":"St. John","year":"1990","journal-title":"Artif. Intell."},{"key":"10.1016\/j.neuron.2017.06.011_bib152","unstructured":"Stachenfeld, K., Botvinick, M.M., and Gershman, S.J. (2014). Design principles of hippocampal cognitive maps. In Advances in Neural Information Processing Systems 27, pp. 2528\u20132536."},{"key":"10.1016\/j.neuron.2017.06.011_bib153","unstructured":"Sukhbaatar, S., Szlam, A., Weston, J., and Fergus, R. (2015). End-to-end memory networks. arXiv, arXiv:150308895."},{"key":"10.1016\/j.neuron.2017.06.011_bib154","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/j.neuron.2006.01.021","article-title":"Orienting attention based on long-term memory experience","volume":"49","author":"Summerfield","year":"2006","journal-title":"Neuron"},{"key":"10.1016\/j.neuron.2017.06.011_bib155","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1145\/122344.122377","article-title":"Dyna, an integrated architecture for learning, planning, and reacting","volume":"2","author":"Sutton","year":"1991","journal-title":"ACM SIGART Bull."},{"key":"10.1016\/j.neuron.2017.06.011_bib156","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1037\/0033-295X.88.2.135","article-title":"Toward a modern theory of adaptive networks: expectation and prediction","volume":"88","author":"Sutton","year":"1981","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib157","series-title":"Reinforcement Learning","author":"Sutton","year":"1998"},{"key":"10.1016\/j.neuron.2017.06.011_bib158","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1145\/203330.203343","article-title":"Temporal difference learning and TD-Gammon","volume":"38","author":"Tesauro","year":"1995","journal-title":"Commun. ACM"},{"key":"10.1016\/j.neuron.2017.06.011_bib159","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0921-8890(95)00004-Y","article-title":"Lifelong robot learning","volume":"15","author":"Thrun","year":"1995","journal-title":"Robot. Auton. Syst."},{"key":"10.1016\/j.neuron.2017.06.011_bib160","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1037\/h0061626","article-title":"Cognitive maps in rats and men","volume":"55","author":"Tolman","year":"1948","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.neuron.2017.06.011_bib161","doi-asserted-by":"crossref","first-page":"12554","DOI":"10.1038\/ncomms12554","article-title":"A dynamic code for economic object valuation in prefrontal cortex neurons","volume":"7","author":"Tsutsui","year":"2016","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuron.2017.06.011_bib178","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1037\/0003-066X.40.4.385","article-title":"How many memory systems are there?","volume":"40","author":"Tulving","year":"1985","journal-title":"American Psychologist"},{"key":"10.1016\/j.neuron.2017.06.011_bib162","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1146\/annurev.psych.53.100901.135114","article-title":"Episodic memory: from mind to brain","volume":"53","author":"Tulving","year":"2002","journal-title":"Annu. Rev. Psychol."},{"key":"10.1016\/j.neuron.2017.06.011_bib163","first-page":"230","article-title":"On computable numbers, with an application to the Entscheidungs problem","volume":"2","author":"Turing","year":"1936","journal-title":"Proc. Lond. Math. Soc."},{"key":"10.1016\/j.neuron.2017.06.011_bib164","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","article-title":"Computing machinery and intelligence","volume":"236","author":"Turing","year":"1950","journal-title":"Mind"},{"key":"10.1016\/j.neuron.2017.06.011_bib165","unstructured":"van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. (2016). WaveNet: a generative model for raw audio. arXiv, arXiv:160903499."},{"key":"10.1016\/j.neuron.2017.06.011_bib166","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D. (2016). Matching networks for one shot learning. arXiv, arXiv:160604080."},{"key":"10.1016\/j.neuron.2017.06.011_bib167","unstructured":"Wang, J., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J.Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M.M. (2016). Learning to reinforcement learn. arXiv, arXiv:161105763."},{"key":"10.1016\/j.neuron.2017.06.011_bib168","series-title":"Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences","author":"Werbos","year":"1974"},{"key":"10.1016\/j.neuron.2017.06.011_bib169","unstructured":"Weston, J., Chopra, S., and Bordes, A. (2014). Memory networks. arXiv, arXiv:14103916."},{"key":"10.1016\/j.neuron.2017.06.011_bib170","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1162\/NECO_a_00949","article-title":"An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity","volume":"29","author":"Whittington","year":"2017","journal-title":"Neural Comput."},{"key":"10.1016\/j.neuron.2017.06.011_bib171","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et\u00a0al. (2016). Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv, arXiv:160908144."},{"key":"10.1016\/j.neuron.2017.06.011_bib172","unstructured":"Xu, K., Kiros, J., Courville, A., Salakhutdinov, R., and Bengio, Y. (2015). Show, attend and tell: neural image caption generation with visual attention. arXiv, arXiv:150203044."},{"key":"10.1016\/j.neuron.2017.06.011_bib173","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1038\/nn.4244","article-title":"Using goal-driven deep learning models to understand sensory cortex","volume":"19","author":"Yamins","year":"2016","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuron.2017.06.011_bib174","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1038\/nature08577","article-title":"Stably maintained dendritic spines are associated with lifelong memories","volume":"462","author":"Yang","year":"2009","journal-title":"Nature"},{"key":"10.1016\/j.neuron.2017.06.011_bib175","unstructured":"Zahavy, T., Zrihem, N.B., and Mannor, S. (2016). Graying the black box: understanding DQNs. arXiv, arXiv:160202658."},{"key":"10.1016\/j.neuron.2017.06.011_bib176","unstructured":"Zaremba, W., and Sutskever, I. (2014). Learning to execute. arXiv, arXiv:1410.4615."}],"container-title":["Neuron"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0896627317305093?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0896627317305093?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T01:18:29Z","timestamp":1659230309000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0896627317305093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7]]},"references-count":178,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,7]]}},"alternative-id":["S0896627317305093"],"URL":"https:\/\/doi.org\/10.1016\/j.neuron.2017.06.011","relation":{"has-review":[{"id-type":"doi","id":"10.3410\/f.727818947.793536418","asserted-by":"object"}]},"ISSN":["0896-6273"],"issn-type":[{"value":"0896-6273","type":"print"}],"subject":[],"published":{"date-parts":[[2017,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Neuroscience-Inspired Artificial Intelligence","name":"articletitle","label":"Article Title"},{"value":"Neuron","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuron.2017.06.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Elsevier Inc.","name":"copyright","label":"Copyright"}]}}