AI mimics brain codes for navigation

Savelli, Francesco; Knierim, James J.

doi:10.1038/d41586-018-04992-7

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09 May 2018

AI mimics brain codes for navigation

An artificial-intelligence technique called deep learning has now been used to model spatial navigation. The system develops a representation of space similar to that of the grid cells found in the mammalian brain.

Francesco Savelli⁰ &
James J. Knierim¹

Francesco Savelli
1. Francesco Savelli is at the Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA.
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James J. Knierim
1. James J. Knierim is at the Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA; and in the Solomon H. Snyder Department of Neuroscience, Johns Hopkins University.
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Deep learning is an approach to artificial intelligence that is inspired by the brain’s neural networks. The technique is contributing to a plethora of technologies, from automated video analysis to language translation. In a paper online in Nature, Banino et al.¹ use this framework to gain insights into real-life neuronal networks — in particular, how geometrically regular representations of space can facilitate flexible navigation strategies.

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Nature 557, 313-314 (2018)

doi: https://doi.org/10.1038/d41586-018-04992-7

References

Banino, A. et al. Nature 557, 429–433 (2018).
Article Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).
Article PubMed CAS Google Scholar
McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I. & Moser, M.-B. Nature Rev. Neurosci. 7, 663–678 (2006).
Article PubMed CAS Google Scholar
Gallistel, C. R. The Organization of Learning (MIT Press, 1990).
Google Scholar
Thrun, S., Burgard, W. & Fox, D. Probabilistic Robotics (MIT Press, 2005).
Google Scholar
Carpenter, F., Manson, D., Jeffery, K., Burgess, N. & Barry, C. Curr. Biol. 25, 1176–1182 (2015).
Article PubMed CAS Google Scholar
Savelli, F., Luck, J. D. & Knierim, J. J. eLife 6, e21354 (2017).
Article PubMed Google Scholar
Koenig, J., Linder, A. N., Leutgeb, J. K. & Leutgeb, S. Science 332, 592–595 (2011).
Article PubMed CAS Google Scholar
Muessig, L., Hauser, J,. Wills, T. J. & Cacucci, F. Neuron 86, 1167–1173 (2015).
Article PubMed CAS Google Scholar
Wang, Y., Romani, S., Lustig, B., Leonardo, A. & Pastalkova, E. Nature Neurosci. 18, 282–288 (2015).
Article PubMed CAS Google Scholar
Mallory, C. S., Hardcastle, K., Bant, J. S. & Giocomo, L. M. Nature Neurosci. 21, 270–82 (2018).
Article PubMed CAS Google Scholar