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Building machines that adapt and compute like brains

  • Nikolaus Kriegeskorte (a1) and Robert M. Mok (a1)

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.

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Aitchison, L. & Lengyel, M. (2016) The Hamiltonian brain: Efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics. PLoS Computational Biology 12(12):e1005186.
DiCarlo, J. J., Zoccolan, D. & Rust, N. C. (2012) How does the brain solve visual object recognition? Neuron 73(3):415–34.
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.
Eliasmith, C. & Trujillo, O. (2014) The use and abuse of large-scale brain models. Current Opinion in Neurobiology 25:16.
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.
Khaligh-Razavi, S. M. & Kriegeskorte, N. (2014) Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology 10(11):e1003915.
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.
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.
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.
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. (2015a) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–38.
Marblestone, A. H., Wayne, G. & Kording, K. P. (2016) Toward an integration of deep learning and neuroscience. Frontiers in Computational Neuroscience 10:94.
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.
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.

Building machines that adapt and compute like brains

  • Nikolaus Kriegeskorte (a1) and Robert M. Mok (a1)

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