Nature - USA (2019-07-18)

(Antfer) #1

Article reSeArcH


that misclassify ‘adversarial images’ that differ imperceptibly from the
training examples^34 ,^35. We suggest that a power-law code that is just
above the critical exponent represents a balance between the efficiency
of high-dimensional codes and the robustness of smooth codes, which
enable generalization.

Online content
Any methods, additional references, Nature Research reporting summaries, source
data, statements of data availability and associated accession codes are available at
https://doi.org/10.1038/s41586-019-1346-5.

Received: 6 August 2018; Accepted: 29 May 2019;
Published online 26 June 2019.


  1. Barlow, H. B. in Sensory Communication (ed. Rosenblith, W.) 217–234 (MIT
    Press, 1961).

  2. Atick, J. J. & Redlich, A. N. Towards a theory of early visual processing. Neural
    Comput. 2 , 308–320 (1990).

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

  4. DiCarlo, J. J., Zoccolan, D. & Rust, N. C. How does the brain solve visual object
    recognition? Neuron 73 , 415–434 (2012).

  5. Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks.
    Nature 497 , 585–590 (2013).

  6. Chung, S., Lee, D. D. & Sompolinsky, H. Classification and geometry of general
    perceptual manifolds. Phys. Rev. X 8 , 031003 (2018).

  7. Cunningham, J. P. & Yu, B. M. Dimensionality reduction for large-scale neural
    recordings. Nat. Neurosci. 17 , 1500–1509 (2014).

  8. Machens, C. K., Romo, R. & Brody, C. D. Functional, but not anatomical, separation
    of “what” and “when” in prefrontal cortex. J. Neurosci. 30 , 350–360 (2010).

  9. Kobak, D. et al. Demixed principal component analysis of neural population
    data. eLife 5 , e10989 (2016).

  10. Archer, E. W., Koster, U., Pillow, J. W. & Macke, J. H. Low-dimensional models of
    neural population activity in sensory cortical circuits. In Proc. 27th International
    Conference on Neural Information Processing Systems (eds Ghahramani, Z. et al.)
    343–351 (Curran, 2014).

  11. Sadtler, P. T. et al. Neural constraints on learning. Nature 512 , 423–426 (2014).

  12. Chapin, J. K. & Nicolelis, M. A. Principal component analysis of neuronal
    ensemble activity reveals multidimensional somatosensory representations.^
    J. Neurosci. Methods 94 , 121–140 (1999).

  13. Bathellier, B., Buhl, D. L., Accolla, R. & Carleton, A. Dynamic ensemble odor
    coding in the mammalian olfactory bulb: sensory information at different
    timescales. Neuron 57 , 586–598 (2008).

  14. Churchland, M. M. et al. Neural population dynamics during reaching. Nature
    487 , 51–56 (2012).

  15. Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent
    computation by recurrent dynamics in prefrontal cortex. Nature 503 , 78–84
    (2013).

  16. Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons:
    implications for connectivity, computation, and information coding. J. Neurosci.
    18 , 3870–3896 (1998).

  17. Reich, D. S., Mechler, F. & Victor, J. D. Independent and redundant information
    in nearby cortical neurons. Science 294 , 2566–2568 (2001).

  18. Gao, P. et al. A theory of multineuronal dimensionality, dynamics and
    measurement. Preprint at https://www.biorxiv.org/content/
    early/2017/11/12/214262 (2017).

  19. Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon
    microscopy. Preprint at https://www.biorxiv.org/content/
    early/2017/07/20/061507 (2016).

  20. Deng, J. et al. Imagenet: A large-scale hierarchical image database. In IEEE
    Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).

  21. Vinje, W. E. & Gallant, J. L. Natural stimulation of the nonclassical receptive field
    increases information transmission efficiency in V1. J. Neurosci. 22 , 2904–2915
    (2002).
    22. Ringach, D. L. Spatial structure and symmetry of simple-cell receptive fields in
    macaque primary visual cortex. J. Neurophysiol. 88 , 455–463 (2002).
    23. Niell, C. M. & Stryker, M. P. Highly selective receptive fields in mouse visual
    cortex. J. Neurosci. 28 , 7520–7536 (2008).
    24. Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is
    inconsistent with temporal integration of random EPSPs. J. Neurosci. 13 ,
    334–350 (1993).
    25. Cossell, L. et al. Functional organization of excitatory synaptic strength in
    primary visual cortex. Nature 518 , 399–403 (2015).
    26. Smyth, D., Willmore, B., Baker, G. E., Thompson, I. D. & Tolhurst, D. J. The
    receptive-field organization of simple cells in primary visual cortex of ferrets
    under natural scene stimulation. J. Neurosci. 23 , 4746–4759 (2003).
    27. Carandini, M. et al. Do we know what the early visual system does? J. Neurosci.
    25 , 10577–10597 (2005).
    28. David, S. V. & Gallant, J. L. Predicting neuronal responses during natural vision.
    Netw. Comput. Neural Syst 16 , 239–260 (2005).
    29. Touryan, J., Felsen, G. & Dan, Y. Spatial structure of complex cell receptive fields
    measured with natural images. Neuron 45 , 781–791 (2005).
    30. de Vries, S. E. J. et al. A large-scale, standardized physiological survey reveals
    higher order coding throughout the mouse visual cortex. Preprint at https://
    http://www.biorxiv.org/content/early/2018/06/29/359513 (2018).
    31. Field, D. J. Relations between the statistics of natural images and the response
    properties of cortical cells. J. Opt. Soc. Am. A 4 , 2379–2394 (1987).
    32. Ruderman, D. L. & Bialek, W. Statistics of natural images: Scaling in the woods.
    In Advances in Neural Information Processing Systems 551–558 (1994).
    33. Tao, T. An Epsilon of Room, I: Real Analysis 1.12.3 (American Mathematical
    Society, 2010).
    34. Szegedy, C. et al. Intriguing properties of neural networks. Preprint at http://
    arxiv.org/abs/1312.6199 (2013).
    35. Goodfellow, I. J., Shlens, J. & Szegedy, C. Explaining and harnessing adversarial
    examples. Preprint at http://arxiv.org/abs/1412.6572 (2014).


Acknowledgements We thank M. Krumin for assistance with the two-photon
microscopes, C. Reddy for surgeries, and K. Falconer, A. Gretton and M. Benna
for discussions of mathematics. This research was funded by Wellcome Trust
Investigator grants (108726, 205093 and 204915) and by a grant from the
Simons Foundation (SCGB 325512). C.S. was funded by a four-year Gatsby
Foundation PhD studentship. M.C. holds the GlaxoSmithKline/Fight for Sight
Chair in Visual Neuroscience. K.D.H. was funded by the European Research
Council (694401). N.S. was supported by postdoctoral fellowships from the
Human Frontier Sciences Program and the Marie Curie Action of the EU
(656528). C.S. and M.P. are now funded by HHMI Janelia.

Reviewer information Nature thanks Jakob Macke, Ken Miller and Byron Yu for
their contribution to the peer review of this work.

Author contributions C.S., M.P., N.S., M.C. and K.D.H. conceptualized the study;
C.S., M.P., N.S. and K.D.H. devised the methodology; C.S. and M.P. designed the
software; C.S., M.P. and N.S. performed experiments; C.S. and M.P. analysed the
data; K.D.H. proved mathematical theorems; C.S., M.P., N.S., M.C. and K.D.H.
wrote the paper; and M.C. and K.D.H. provided resources and acquired funding.

Competing interests The authors declare no competing interests.

Additional information
Extended data is available for this paper at https://doi.org/10.1038/s41586-
019-1346-5.
Supplementary information is available for this paper at https://doi.org/
10.1038/s41586-019-1346-5.
Reprints and permissions information is available at http://www.nature.com/
reprints.
Correspondence and requests for materials should be addressed to C.S., M.P.
or K.D.H.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.

© The Author(s), under exclusive licence to Springer Nature Limited 2019

18 JUlY 2019 | VOl 571 | NAtUre | 365
Free download pdf