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Cognitive computational neuroscience

Abstract

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. Here we review recent work in the intersection of cognitive science, computational neuroscience and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.

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Fig. 1: Modern imaging techniques provide unprecedentedly detailed information about brain activity, but data-driven analyses support only limited insights.
Fig. 2: What does it mean to understand how the brain works?
Fig. 3: The space of process models.

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References

  1. Newell, A. You can’t play 20 questions with nature and win: projective comments on the papers of this symposium. Technical Report, School of Computer Science, Carnegie Mellon University (1973).

    Chapter  Google Scholar 

  2. Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017).

    Article  PubMed  Google Scholar 

  3. Kriegeskorte, N. & Mok, R. M. Building machines that adapt and compute like brains. Behav. Brain Sci. 40, e269 (2017).

    Article  PubMed  Google Scholar 

  4. Simon, H. A. & Newell, A. Human problem solving: the state of the theory in 1970. Am. Psychol. 26, 145–159 (1971).

    Article  Google Scholar 

  5. Anderson, J. R. The Architecture of Cognition (Harvard Univ. Press, Cambridge, MA, USA, 1983).

  6. McClelland, J. L. & Rumelhart, D. E. Parallel Distributed Processing (MIT Press, Cambridge, MA, USA, 1987).

  7. Gazzaniga, M. S. ed. The Cognitive Neurosciences (MIT Press, Cambridge, MA, USA, 2004).

  8. Fodor, J. A. Précis of The Modularity of Mind. Behav. Brain Sci. 8, 1 (1985).

    Article  Google Scholar 

  9. Chklovskii, D. B. & Koulakov, A. A. Maps in the brain: what can we learn from them? Annu. Rev. Neurosci. 27, 369–392 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Szucs, D. & Ioannidis, J. P. A. Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLoS Biol. 15, e2000797 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kanwisher, N., McDermott, J. & Chun, M. M. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17, 4302–4311 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tsao, D. Y., Freiwald, W. A., Tootell, R. B. & Livingstone, M. S. A cortical region consisting entirely of face-selective cells. Science 311, 670–674 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Freiwald, W. A. & Tsao, D. Y. Functional compartmentalization and viewpoint generalization within the macaque face-processing system. Science 330, 845–851 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Grill-Spector, K., Weiner, K. S., Kay, K. & Gomez, J. The functional neuroanatomy of human face perception. Annu. Rev. Vis. Sci. 3, 167–196 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yildirim, I. et al. Efficient and robust analysis-by-synthesis in vision: a computational framework, behavioral tests, and modeling neuronal representations. in Annual Conference of the Cognitive Science Society (eds. Noelle, D. C. et al.) (Cognitive Science Society, Austin, TX, USA, 2015).

  17. Kriegeskorte, N., Formisano, E., Sorger, B. & Goebel, R. Individual faces elicit distinct response patterns in human anterior temporal cortex. Proc. Natl Acad. Sci. USA 104, 20600–20605 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Anzellotti, S., Fairhall, S. L. & Caramazza, A. Decoding representations of face identity that are tolerant to rotation. Cereb. Cortex 24, 1988–1995 (2014).

    Article  PubMed  Google Scholar 

  19. Chang, L. & Tsao, D. Y. The code for facial identity in the primate brain. Cell 169, 1013–1028.e14 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Van Essen, D. C. et al. The Brain Analysis Library of Spatial maps and Atlases (BALSA) database. Neuroimage 144(Pt. B), 270–274 (2017).

    Article  PubMed  Google Scholar 

  21. Griffiths, T. L., Chater, N., Kemp, C., Perfors, A. & Tenenbaum, J. B. Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci. 14, 357–364 (2010).

    Article  PubMed  Google Scholar 

  22. Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    Article  CAS  PubMed  Google Scholar 

  23. Weiss, Y., Simoncelli, E. P. & Adelson, E. H. Motion illusions as optimal percepts. Nat. Neurosci. 5, 598–604 (2002).

    Article  CAS  PubMed  Google Scholar 

  24. Körding, K. P. & Wolpert, D. M. Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004).

    Article  PubMed  CAS  Google Scholar 

  25. MacKay, D. J. C. Information Theory, Inference, and Learning Algorithms. (Cambridge Univ. Press, Cambridge, 2003)

  26. Murphy, K. P. Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA, USA, 2012).

    Google Scholar 

  27. Dayan, P. & Abbott, L. F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (MIT Press, Cambridge, MA, USA, 2001).

    Google Scholar 

  28. Abbott, L. F. Theoretical neuroscience rising. Neuron 60, 489–495 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Olshausen, B. A. & Field, D. J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 14, 481–487 (2004).

    Article  CAS  PubMed  Google Scholar 

  30. Simoncelli, E. P. & Olshausen, B. A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

    Article  CAS  PubMed  Google Scholar 

  31. Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Chaudhuri, R. & Fiete, I. Computational principles of memory. Nat. Neurosci. 19, 394–403 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Shadlen, M. N. & Kiani, R. Decision making as a window on cognition. Neuron 80, 791–806 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).

    Article  CAS  PubMed  Google Scholar 

  35. Wang, X.-J. Decision making in recurrent neuronal circuits. Neuron 60, 215–234 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Diedrichsen, J., Shadmehr, R. & Ivry, R. B. The coordination of movement: optimal feedback control and beyond. Trends Cogn. Sci. 14, 31–39 (2010).

    Article  PubMed  Google Scholar 

  37. Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1, 417–446 (2015).

    Article  PubMed  Google Scholar 

  38. Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. in Advances in Neural Information Processing Systems 25 1097–1105 (Curran Associates, Red Hook, NY, USA, 2012).

  40. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Cohen, J. D. et al. Computational approaches to fMRI analysis. Nat. Neurosci. 20, 304–313 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Forstmann, B. U., Wagenmakers, E.-J., Eichele, T., Brown, S. & Serences, J. T. Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract? Trends Cogn. Sci. 15, 272–279 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).

    Article  CAS  PubMed  Google Scholar 

  47. Hyvarinen, A., Karhunen, J. & Oja, E. Independent Component Analysis (Wiley, Hoboken, NJ, USA, 2001).

  48. Bullmore, E. T. & Bassett, D. S. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113–140 (2011).

    Article  PubMed  Google Scholar 

  49. Deco, G., Jirsa, V. K. & McIntosh, A. R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci. 12, 43–56 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Friston, K. Dynamic causal modeling and Granger causality. Comments on: the identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage 58, 303–305 (2011). author reply 310–311.

    Article  PubMed  Google Scholar 

  51. Dennett, D. C. The Intentional Stance (MIT Press, Cambridge, MA, USA, 1987).

    Google Scholar 

  52. Diedrichsen, J. & Kriegeskorte, N. Representational models: a common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Comput. Biol. 13, e1005508 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Afraz, S.-R., Kiani, R. & Esteky, H. Microstimulation of inferotemporal cortex influences face categorization. Nature 442, 692–695 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. Parvizi, J. et al. Electrical stimulation of human fusiform face-selective regions distorts face perception. J. Neurosci. 32, 14915–14920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424–430 (2006).

    Article  PubMed  Google Scholar 

  56. Tong, F. & Pratte, M. S. Decoding patterns of human brain activity. Annu. Rev. Psychol. 63, 483–509 (2012).

    Article  PubMed  Google Scholar 

  57. Kriegeskorte, N. & Kievit, R. A. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17, 401–412 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37, 435–456 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Haynes, J.-D. A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron 87, 257–270 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Jin, X. & Costa, R. M. Shaping action sequences in basal ganglia circuits. Curr. Opin. Neurobiol. 33, 188–196 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. DiCarlo, J. J. & Cox, D. D. Untangling invariant object recognition. Trends Cogn. Sci. 11, 333–341 (2007).

    Article  PubMed  Google Scholar 

  62. Naselaris, T. & Kay, K. N. Resolving ambiguities of MVPA using explicit models of representation. Trends Cogn. Sci. 19, 551–554 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Mitchell, T. M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).

    Article  CAS  PubMed  Google Scholar 

  64. Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dumoulin, S. O. & Wandell, B. A. Population receptive field estimates in human visual cortex. Neuroimage 39, 647–660 (2008).

    Article  PubMed  Google Scholar 

  66. Diedrichsen, J., Ridgway, G. R., Friston, K. J. & Wiestler, T. Comparing the similarity and spatial structure of neural representations: a pattern-component model. Neuroimage 55, 1665–1678 (2011).

    Article  PubMed  Google Scholar 

  67. Kriegeskorte, N., Mur, M. & Bandettini, P. Representational similarity analysis - connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Nili, H. et al. A toolbox for representational similarity analysis. PLoS Comput. Biol. 10, e1003553 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Devereux, B. J., Clarke, A., Marouchos, A. & Tyler, L. K. Representational similarity analysis reveals commonalities and differences in the semantic processing of words and objects. J. Neurosci. 33, 18906–18916 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Markram, H. The Blue Brain Project. Nat. Rev. Neurosci. 7, 153–160 (2006).

    Article  CAS  PubMed  Google Scholar 

  72. Eliasmith, C. & Trujillo, O. The use and abuse of large-scale brain models. Curr. Opin. Neurobiol. 25, 1–6 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Eliasmith, C. et al. A large-scale model of the functioning brain. Science 338, 1202–1205 (2012).

    Article  CAS  PubMed  Google Scholar 

  74. Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).

    Article  CAS  PubMed  Google Scholar 

  75. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Article  Google Scholar 

  76. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016).

    Google Scholar 

  77. Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Khaligh-Razavi, S.-M. & Kriegeskorte, N. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput. Biol. 10, e1003915 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Cadieu, C. F. et al. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput. Biol. 10, e1003963 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Güçlü, U. & van Gerven, M. A. J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  81. Eickenberg, M., Gramfort, A., Varoquaux, G. & Thirion, B. Seeing it all: convolutional network layers map the function of the human visual system. Neuroimage 152, 184–194 (2017).

    Article  PubMed  Google Scholar 

  82. Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A. & Oliva, A. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6, 27755 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hong, H., Yamins, D. L. K., Majaj, N. J. & DiCarlo, J. J. Explicit information for category-orthogonal object properties increases along the ventral stream. Nat. Neurosci. 19, 613–622 (2016).

    Article  CAS  PubMed  Google Scholar 

  84. Kubilius, J., Bracci, S. & Op de Beeck, H. P. Deep neural networks as a computational model for human shape sensitivity. PLoS Comput. Biol. 12, e1004896 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Jozwik, K. M., Kriegeskorte, N., Storrs, K. R. & Mur, M. Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgments. Front. Psychol. 8, 1726 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Moore, C. & Mertens, S. The Nature of Computation. (Oxford Univ. Press, Oxford, 2011).

    Book  Google Scholar 

  87. Borst, J., Taatgen & Anderson, J. Using the ACT-R cognitive architecture in combination with fMRI data. in An Introduction to Model-Based Cognitive Neuroscience (eds. Forstmann, B. U. & Wagenmakers, E.-J.) (Springer, New York, 2014).

  88. Sutton, R. & Barto, A. Reinforcement Learning: An Introduction Vol. 1 (MIT Press, Cambridge, MA, USA, 1998).

    Google Scholar 

  89. O’Doherty, J. P., Cockburn, J. & Pauli, W. M. Learning, reward, and decision making. Annu. Rev. Psychol. 68, 73–100 (2017).

    Article  PubMed  Google Scholar 

  90. Daw, N. D. & Dayan, P. The algorithmic anatomy of model-based evaluation. Phil. Trans. R. Soc. Lond. B 369, 20130478 (2014).

    Article  Google Scholar 

  91. Lengyel, M. & Dayan, P. Hippocampal contributions to control: the third way in Advances in Neural Information Processing Systems 20 889–896 (MIT Press, Cambridge, MA, USA, 2008)..

  92. Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annu. Rev. Psychol. 68, 101–128 (2017).

    Article  PubMed  Google Scholar 

  93. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    Article  CAS  PubMed  Google Scholar 

  94. Sutton, R. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. in Proceedings of the Seventh International Conference on Machine Learning 216–224 (Morgan Kaufmann, San Francisco, 1990).

  95. Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8, 1704–1711 (2005).

    Article  CAS  PubMed  Google Scholar 

  96. Ma, W. J. Organizing probabilistic models of perception. Trends Cogn. Sci. 16, 511–518 (2012).

    Article  PubMed  Google Scholar 

  97. Fiser, J., Berkes, P., Orbán, G. & Lengyel, M. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119–130 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. How to grow a mind: statistics, structure, and abstraction. Science 331, 1279–1285 (2011).

    Article  CAS  PubMed  Google Scholar 

  99. Tversky, A. & Kahneman, D. Judgment under uncertainty: heuristics and biases. in Utility, Probability, and Human Decision Making (eds. Wendt, D. & Vlek, C.) 141–162, https://doi.org/10.1007/978-94-010-1834-0_8 (Springer Netherlands, Dordrecht, the Netherlands, 1975).

    Chapter  Google Scholar 

  100. Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. Human-level concept learning through probabilistic program induction. Science 350, 1332–1338 (2015).

    Article  CAS  PubMed  Google Scholar 

  101. Ullman, T. D., Spelke, E., Battaglia, P. & Tenenbaum, J. B. Mind games: game engines as an architecture for intuitive physics. Trends Cogn. Sci. 21, 649–665 (2017).

    Article  PubMed  Google Scholar 

  102. Battaglia, P. W., Hamrick, J. B. & Tenenbaum, J. B. Simulation as an engine of physical scene understanding. Proc. Natl Acad. Sci. USA 110, 18327–18332 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Kubricht, J. R., Holyoak, K. J. & Lu, H. Intuitive physics: current research and controversies. Trends Cogn. Sci. 21, 749–759 (2017).

    Article  PubMed  Google Scholar 

  104. Pantelis, P. C. et al. Inferring the intentional states of autonomous virtual agents. Cognition 130, 360–379 (2014).

    Article  PubMed  Google Scholar 

  105. Pouget, A., Beck, J. M., Ma, W. J. & Latham, P. E. Probabilistic brains: knowns and unknowns. Nat. Neurosci. 16, 1170–1178 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Orhan, A. E. & Ma, W. J. Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback. Nat. Commun. 8, 138 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Tervo, D. G. R., Tenenbaum, J. B. & Gershman, S. J. Toward the neural implementation of structure learning. Curr. Opin. Neurobiol. 37, 99–105 (2016).

    Article  CAS  PubMed  Google Scholar 

  108. Buesing, L., Bill, J., Nessler, B. & Maass, W. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput. Biol. 7, e1002211 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Haefner, R. M., Berkes, P. & Fiser, J. Perceptual decision-making as probabilistic inference by neural sampling. Neuron 90, 649–660 (2016).

    Article  CAS  PubMed  Google Scholar 

  110. Aitchison, L. & Lengyel, M. The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics. PLoS Comput. Biol. 12, e1005186 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  111. Sanborn, A. N. & Chater, N. Bayesian brains without probabilities. Trends Cogn. Sci. 20, 883–893 (2016).

    Article  PubMed  Google Scholar 

  112. Dasgupta, I., Schulz, E., Goodman, N. & Gershman, S. Amortized hypothesis generation. Preprint at bioRxiv https://doi.org/10.1101/137190 (2017).

  113. Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A. & Poeppel, D. Neuroscience needs behavior: correcting a reductionist bias. Neuron 93, 480–490 (2017).

    Article  CAS  PubMed  Google Scholar 

  114. Gomez-Marin, A., Paton, J. J., Kampff, A. R., Costa, R. M. & Mainen, Z. F. Big behavioral data: psychology, ethology and the foundations of neuroscience. Nat. Neurosci. 17, 1455–1462 (2014).

    Article  CAS  PubMed  Google Scholar 

  115. Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (MIT Press, Cambridge, MA, USA, 2010).

    Book  Google Scholar 

  116. Love, B. C. The algorithmic level is the bridge between computation and brain. Top. Cogn. Sci. 7, 230–242 (2015).

    Article  PubMed  Google Scholar 

  117. Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Preprint at https://arxiv.org/abs/1506.02142 (2016).

  118. Rezende, D., Mohamed, S., Danihelka, I., Gregor, K. & Wierstra, D. One-shot generalization in deep generative models. Proc. Int. Conf. Mach. Learn. Appl. 48, 1521–1529 (2016).

    Google Scholar 

  119. Kingma, D. & Welling, M. Auto-encoding variational Bayes. Preprint at https://arxiv.org/abs/1312.6114 (2013).

  120. Naselaris, T. et al. Cognitive Computational Neuroscience: a new conference for an emerging discipline. Trends Cogn. Sci. 22, 365–367 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Ahrens, M. B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Kietzmann, T., McClure, P. & Kriegeskorte, N. Deep neural networks in computational neuroscience. Preprint at bioRxiv https://doi.org/10.1101/133504 (2017).

  123. Hornik, K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251–257 (1991).

    Article  Google Scholar 

  124. Wyatte, D., Curran, T. & O’Reilly, R. The limits of feedforward vision: recurrent processing promotes robust object recognition when objects are degraded. J. Cogn. Neurosci. 24, 2248–2261 (2012).

    Article  PubMed  Google Scholar 

  125. Spoerer, C. J., McClure, P. & Kriegeskorte, N. Recurrent convolutional neural networks: a better model of biological object recognition. Front. Psychol. 8, 1551 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Hunt, L. T. & Hayden, B. Y. A distributed, hierarchical and recurrent framework for reward-based choice. Nat. Rev. Neurosci. 18, 172–182 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Schäfer, A. M. & Zimmermann, H. G. Recurrent neural networks are universal approximators. Int. J. Neural Syst. 17, 253–263 (2007).

    Article  PubMed  Google Scholar 

  128. O’Reilly, R. C., Hazy, T. E., Mollick, J., Mackie, P. & Herd, S. Goal-driven cognition in the brain: a computational framework. Preprint at http://arxiv.org/abs/1404.7591 (2014).

  129. Whittington, J. C. R. & Bogacz, R. An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity. Neural Comput. 29, 1229–1262 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Schiess, M., Urbanczik, R. & Senn, W. Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites. PLoS Comput. Biol. 12, e1004638 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Marblestone, A. H., Wayne, G. & Kording, K. P. Towards an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10, 94 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Shadlen, M. N. & Shohamy, D. Decision making and sequential sampling from memory. Neuron 90, 927–939 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Roelfsema, P. R. & van Ooyen, A. Attention-gated reinforcement learning of internal representations for classification. Neural Comput. 17, 2176–2214 (2005).

    Article  PubMed  Google Scholar 

  134. Goodfellow, I. et al. Generative adversarial nets. Preprint at https://arxiv.org/abs/1406.2661 (2014).

  135. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A. & Hudspeth, A. J. Principles of Neural Science (McGraw-Hill Professional, New York, 2013).

  136. Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–151 (2013).

    Article  CAS  PubMed  Google Scholar 

  138. Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005).

    Article  PubMed  Google Scholar 

  139. Kumaran, D., Hassabis, D. & McClelland, J. L. What learning systems do intelligent agents need? complementary learning systems theory updated. Trends Cogn. Sci. 20, 512–534 (2016).

    Article  PubMed  Google Scholar 

  140. Yuille, A. & Kersten, D. Vision as Bayesian inference: analysis by synthesis? Trends Cogn. Sci. 10, 301–308 (2006).

    Article  PubMed  Google Scholar 

  141. Helmholtz, H. Handbuch der physiologischen Optik (Dover, New York, 1860).

    Google Scholar 

  142. Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: a converging paradigm for intelligence in brains, minds, and machines. Science 349, 273–278 (2015).

    Article  CAS  PubMed  Google Scholar 

  143. Simon, H. A. Bounded rationality. in Utility and Probability (eds. Eatwell, J., Milgate, M. & Newman, P.) 15–18, https://doi.org/10.1007/978-1-349-20568-4_5 (Palgrave Macmillan, London, 1990).

    Chapter  Google Scholar 

  144. Griffiths, T. L., Lieder, F. & Goodman, N. D. Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Top. Cogn. Sci. 7, 217–229 (2015).

    Article  PubMed  Google Scholar 

  145. Srikumar, V., Kundu, G. & Roth, D. On amortizing inference cost for structured prediction Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning 1114–1124 (Association for Computational Linguistics, Stroudsburg, PA, USA, 2012).

  146. Bengio, Y., Scellier, B., Bilaniuk, O., Sacramento, J. & Senn, W. Feedforward initialization for fast inference of deep generative networks is biologically plausible. Preprint at https://arxiv.org/abs/1606.01651 (2016).

  147. Ghahramani, Z. Bayesian non-parametrics and the probabilistic approach to modelling. Philos. Trans. A Math. Phys. Eng. Sci. 371, 20110553 (2012).

    Article  PubMed  Google Scholar 

  148. Deng, J. et al. ImageNet: a large-scale hierarchical image database. in 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255, https://doi.org/10.1109/CVPR.2009.5206848 (IEEE, Piscataway, NJ, USA, 2009).

  149. Beattie, C. et al. DeepMind Lab. Preprint at https://arxiv.org/abs/1612.03801 (2016).

  150. Griffiths, T. L. Manifesto for a new (computational) cognitive revolution. Cognition 135, 21–23 (2015).

    Article  PubMed  Google Scholar 

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Acknowledgements

This paper benefited from discussions in the context of the new conference Cognitive Computational Neuroscience, which had its inaugural meeting in New York City in September 2017120. We are grateful in particular to T. Naselaris, K. Kay, K. Kording, D. Shohamy, R. Poldrack, J. Diedrichsen, M. Bethge, R. Mok, T. Kietzmann, K. Storrs, M. Mur, T. Golan, M. Lengyel, M. Shadlen, D. Wolpert, A. Oliva, D. Yamins, J. Cohen, J. DiCarlo, T. Konkle, J. McDermott, N. Kanwisher, S. Gershman and J. Tenenbaum for inspiring discussions.

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Kriegeskorte, N., Douglas, P.K. Cognitive computational neuroscience. Nat Neurosci 21, 1148–1160 (2018). https://doi.org/10.1038/s41593-018-0210-5

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