Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T11:26:53.746Z Has data issue: false hasContentIssue false
  • English
  • Français

Building machines that adapt and compute like brains

Published online by Cambridge University Press:  10 November 2017

Nikolaus Kriegeskorte  and
Robert M. Mok
Nikolaus Kriegeskorte
Affiliation:
Medical Research Council Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, United Kingdom. [email protected]@mrc-cbu.cam.ac.uk
Robert M. Mok
Affiliation:
Medical Research Council Cognition and Brain Sciences Unit, Cambridge, CB2 7EF, United Kingdom. [email protected]@mrc-cbu.cam.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neural-level models, understand their relationships, and test both types of models with both brain and behavioral data.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e269
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aitchison, L. & Lengyel, M. (2016) The Hamiltonian brain: Efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics. PLoS Computational Biology 12(12):e1005186.Google Scholar
DiCarlo, J. J., Zoccolan, D. & Rust, N. C. (2012) How does the brain solve visual object recognition? Neuron 73(3):415–34.CrossRefGoogle ScholarPubMed
Eickenberg, M., Gramfort, A., Varoquaux, G. & Thirion, B. (2016) Seeing it all: Convolutional network layers map the function of the human visual system. NeuroImage 2017;152:184–94.Google Scholar
Eliasmith, C. & Trujillo, O. (2014) The use and abuse of large-scale brain models. Current Opinion in Neurobiology 25:1–6.Google Scholar
Güçlü, U. & van Gerven, M. A. J. (2015) Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience 35(27):10005–14.Google Scholar
Khaligh-Razavi, S. M. & Kriegeskorte, N. (2014) Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology 10(11):e1003915.CrossRefGoogle Scholar
Kriegeskorte, N. & Diedrichsen, J. (2016) Inferring brain-computational mechanisms with models of activity measurements. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences 371(1705):489–95.Google Scholar
Kriegeskorte, N., Mur, M. & Bandettini, P. (2008) Representational similarity analysis – Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience 2:4. doi: 10.3389/neuro.06.004.2008.Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Presented at the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, December 3–6, 2012. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), ed. Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q., pp. 1097–105. Neural Information Processing Systems Foundation.Google Scholar
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. (2015a) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–38.CrossRefGoogle ScholarPubMed
Marblestone, A. H., Wayne, G. & Kording, K. P. (2016) Toward an integration of deep learning and neuroscience. Frontiers in Computational Neuroscience 10:94.Google Scholar
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D. & DiCarlo, J. J. (2014) Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences of the United States of America 111(23):8619–24.Google Scholar
Yildirim, I., Kulkarni, T. D., Freiwald, W. A. & Tenenbaum, J. (2015) Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and comparison with neural representations. In: Proceedings of the 37th Annual Conference of the Cognitive Science Society, Pasadena, CA, July 22–25, 2015. Cognitive Science Society. Available at: https://mindmodeling.org/cogsci2015/papers/0471/index.html.Google Scholar