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NEUROSCIENCE

Deep learning reaches the motor system

Nature Methods volume 15, pages 772–773 (2018)Cite this article

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A new article by Pandarinath et al. describes an artificial neural network model that captures some key aspects of the activity of populations of neurons in the primary motor cortex.

When can we declare that we understand something? One pragmatically minded definition is that we understand a phenomenon when we can duplicate it. Thus, the airfoil design of an airplane wing reveals a better understanding of bird flight than did the feathered wings of mythology’s Icarus. This intuition animates an important direction in systems neuroscience: the use of artificial neural networks to duplicate the input–output relationship of real networks of neurons, with the hope that such networks will inform on internal neurobiological mechanisms. Even with different physical implementations (in this case a computer versus cortical tissue), if we can mimic the functionality of a population of motor cortex neurons, then we can claim that we have begun to understand how those neurons work. This form of understanding by model building is a tale of successive approximations: over time we build models that capture more and more details of a system’s function, and are increasingly constrained by the biological system’s internal structure. This approach, which has been influential in efforts to obtain a mechanistic understanding of the visual system1,2, has now begun to bear fruit as a tool for investigating the brain’s motor system3.

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References

  1. Yamins, D. L. K. et al. Proc. Natl. Acad. Sci. USA 111, 8619–8624 (2014).

    Article  CAS  Google Scholar 

  2. Yamins, D. L, Hong, H., Cadieu, C. & DiCarlo, J. J. in Advances in Neural Information Processing Systems 26 (eds Burges, C. J. C. et al.) 3093–3101 (NIPS Foundation, La Jolla, CA, 2013).

  3. Pandarinath, C. et al. Nat. Methods. https://doi.org/10.1038/s41592-018-0109-9 (2018).

    Article  PubMed  Google Scholar 

  4. Churchland, M. M. et al. Nature 487, 51–56 (2012).

    Article  CAS  Google Scholar 

  5. Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I. & Shenoy, K. V. Neuron 68, 387–400 (2010).

    Article  CAS  Google Scholar 

  6. Cunningham, J. P. & Yu, B. M. Nat. Neurosci. 17, 1500–1509 (2014).

    Article  CAS  Google Scholar 

  7. Sadtler, P. T. et al. Nature 512, 423–426 (2014).

    Article  CAS  Google Scholar 

  8. Peixoto, D. et al. bioRxiv Preprint at https://www.biorxiv.org/content/early/2018/03/17/283960 (2018).

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Authors and Affiliations

  1. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Aaron P. Batista

  2. Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    James J. DiCarlo

Authors
  1. Aaron P. Batista
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  2. James J. DiCarlo
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Correspondence to Aaron P. Batista.

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The authors declare no competing interests

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Batista, A.P., DiCarlo, J.J. Deep learning reaches the motor system. Nat Methods 15, 772–773 (2018). https://doi.org/10.1038/s41592-018-0152-6

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  • DOI: https://doi.org/10.1038/s41592-018-0152-6

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