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Cognitive computational neuroscience

Nature Neurosciencevolume 21pages11481160 (2018) | Download Citation

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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References

  1. 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).

  2. 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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 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).

  11. 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).

  12. 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).

  13. 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).

  14. 14.

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

  15. 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).

  16. 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. 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).

  18. 18.

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

  19. 19.

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

  20. 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).

  21. 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).

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

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

  30. 30.

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

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

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

  35. 35.

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

  36. 36.

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

  37. 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).

  38. 38.

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

  39. 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. 40.

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

  41. 41.

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

  42. 42.

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

  43. 43.

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

  44. 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).

  45. 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).

  46. 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).

  47. 47.

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

  48. 48.

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

  49. 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).

  50. 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.

  51. 51.

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

  52. 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).

  53. 53.

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

  54. 54.

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

  55. 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).

  56. 56.

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

  57. 57.

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

  58. 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).

  59. 59.

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

  60. 60.

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

  61. 61.

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

  62. 62.

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

  63. 63.

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

  64. 64.

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

  65. 65.

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

  66. 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).

  67. 67.

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

  68. 68.

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

  69. 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).

  70. 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).

  71. 71.

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

  72. 72.

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

  73. 73.

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

  74. 74.

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

  75. 75.

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

  76. 76.

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

  77. 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).

  78. 78.

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

  79. 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).

  80. 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).

  81. 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).

  82. 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).

  83. 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).

  84. 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).

  85. 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).

  86. 86.

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

  87. 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. 88.

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

  89. 89.

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

  90. 90.

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

  91. 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. 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).

  93. 93.

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

  94. 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. 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).

  96. 96.

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

  97. 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).

  98. 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).

  99. 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).

  100. 100.

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

  101. 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).

  102. 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).

  103. 103.

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

  104. 104.

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

  105. 105.

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

  106. 106.

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

  107. 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).

  108. 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).

  109. 109.

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

  110. 110.

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

  111. 111.

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

  112. 112.

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

  113. 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).

  114. 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).

  115. 115.

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

  116. 116.

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

  117. 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. 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).

  119. 119.

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

  120. 120.

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

  121. 121.

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

  122. 122.

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

  123. 123.

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

  124. 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).

  125. 125.

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

  126. 126.

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

  127. 127.

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

  128. 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. 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).

  130. 130.

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

  131. 131.

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

  132. 132.

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

  133. 133.

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

  134. 134.

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

  135. 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. 136.

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

  137. 137.

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

  138. 138.

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

  139. 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).

  140. 140.

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

  141. 141.

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

  142. 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).

  143. 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).

  144. 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).

  145. 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. 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. 147.

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

  148. 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. 149.

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

  150. 150.

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

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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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  1. Department of Psychology, Department of Neuroscience, Department of Electrical Engineering, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    • Nikolaus Kriegeskorte
  2. Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA

    • Pamela K. Douglas

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The authors declare no competing interests.

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Correspondence to Nikolaus Kriegeskorte.

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https://doi.org/10.1038/s41593-018-0210-5

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