Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Optimal representation of sensory information by neural populations

Nature Neuroscience volume 9, pages 690–696 (2006)Cite this article

This article has been updated

Abstract

Sensory information is encoded by populations of neurons. The responses of individual neurons are inherently noisy, so the brain must interpret this information as reliably as possible. In most situations, the optimal strategy for decoding the population signal is to compute the likelihoods of the stimuli that are consistent with an observed neural response. But it has not been clear how the brain can directly compute likelihoods. Here we present a simple and biologically plausible model that can realize the likelihood function by computing a weighted sum of sensory neuron responses. The model provides the basis for an optimal decoding of sensory information. It explains a variety of psychophysical observations on detection, discrimination and identification, and it also directly predicts the relative contributions that different sensory neurons make to perceptual judgments.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Computing the log likelihood function in a feedforward network.
Figure 2: Computing likelihood for the direction of motion.
Figure 3: Predictions of the model for behavioral performance in psychophysical tasks.
Figure 4: Contributions of MT signals to two-choice motion discrimination.

Similar content being viewed by others

Change history

  • 01 June 2006

    File replaced

Notes

  1. *NOTE: In the supplementary information initially published online to accompany this article, equation 5 and the last two equations on page 4 contained typographical errors. The errors have been corrected online.

References

  1. Pouget, A., Dayan, P. & Zemel, R.S. Inference and computation with population codes. Annu. Rev. Neurosci. 26, 381–410 (2003).

    Article  CAS  Google Scholar 

  2. Georgopoulos, A.P., Kalaska, J.F., Caminiti, R. & Massey, J.T. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J. Neurosci. 2, 1527–1537 (1982).

    Article  CAS  Google Scholar 

  3. Deneve, S., Latham, P.E. & Pouget, A. Reading population codes: a neural implementation of ideal observers. Nat. Neurosci. 2, 740–745 (1999).

    Article  CAS  Google Scholar 

  4. Ernst, M.O. & Banks, M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    Article  CAS  Google Scholar 

  5. Hillis, J.M., Watt, S.J., Landy, M.S. & Banks, M.S. Slant from texture and disparity cues: optimal cue combination. J. Vis. 4, 967–992 (2004).

    PubMed  Google Scholar 

  6. Weiss, Y., Simoncelli, E.P. & Adelson, E.H. Motion illusions as optimal percepts. Nat. Neurosci. 5, 598–604 (2002).

    Article  CAS  Google Scholar 

  7. Seung, H.S. & Sompolinsky, H. Simple models for reading neuronal population codes. Proc. Natl. Acad. Sci. USA 90, 10749–10753 (1993).

    Article  CAS  Google Scholar 

  8. 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).

    Article  CAS  Google Scholar 

  9. 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).

    Article  CAS  Google Scholar 

  10. Zohary, E., Shadlen, M.N. & Newsome, W.T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143 (1994).

    Article  CAS  Google Scholar 

  11. Bair, W., Zohary, E. & Newsome, W.T. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J. Neurosci. 21, 1676–1697 (2001).

    Article  CAS  Google Scholar 

  12. Kohn, A. & Smith, M.A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25, 3661–3673 (2005).

    Article  CAS  Google Scholar 

  13. Shamir, M. & Sompolinsky, H. Nonlinear population codes. Neural Comput. 16, 1105–1136 (2004).

    Article  Google Scholar 

  14. Britten, K.H., Shadlen, M.N., Newsome, W.T. & Movshon, J.A. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745–4765 (1992).

    Article  CAS  Google Scholar 

  15. Maunsell, J.H. & van Essen, D.C. The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. J. Neurosci. 3, 2563–2586 (1983).

    Article  CAS  Google Scholar 

  16. Salzman, C.D., Murasugi, C.M., Britten, K.H. & Newsome, W.T. Microstimulation in visual area MT: effects on direction discrimination performance. J. Neurosci. 12, 2331–2355 (1992).

    Article  CAS  Google Scholar 

  17. Salzman, C.D. & Newsome, W.T. Neural mechanisms for forming a perceptual decision. Science 264, 231–237 (1994).

    Article  CAS  Google Scholar 

  18. Mardia, K.V. Statistics of Directional Data (Academic Press, London, New York, 1972).

    Google Scholar 

  19. Swindale, N.V. Orientation tuning curves: empirical description and estimation of parameters. Biol. Cybern. 78, 45–56 (1998).

    Article  CAS  Google Scholar 

  20. Britten, K.H. & Newsome, W.T. Tuning bandwidths for near-threshold stimuli in area MT. J. Neurophysiol. 80, 762–770 (1998).

    Article  CAS  Google Scholar 

  21. Britten, K.H., Shadlen, M.N., Newsome, W.T. & Movshon, J.A. Responses of neurons in macaque MT to stochastic motion signals. Vis. Neurosci. 10, 1157–1169 (1993).

    Article  CAS  Google Scholar 

  22. Adelson, E.H. & Bergen, J.R. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).

    Article  CAS  Google Scholar 

  23. Shadlen, M.N., Britten, K.H., Newsome, W.T. & Movshon, J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486–1510 (1996).

    Article  CAS  Google Scholar 

  24. Regan, D. & Beverley, K.I. Postadaptation orientation discrimination. J. Opt. Soc. Am. A 2, 147–155 (1985).

    Article  CAS  Google Scholar 

  25. Hol, K. & Treue, S. Different populations of neurons contribute to the detection and discrimination of visual motion. Vision Res. 41, 685–689 (2001).

    Article  CAS  Google Scholar 

  26. Britten, K.H., Newsome, W.T., Shadlen, M.N., Celebrini, S. & Movshon, J.A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).

    Article  CAS  Google Scholar 

  27. Purushothaman, G. & Bradley, D.C. Neural population code for fine perceptual decisions in area MT. Nat. Neurosci. 8, 99–106 (2005).

    Article  CAS  Google Scholar 

  28. Salinas, E. & Abbott, L.F. Vector reconstruction from firing rates. J. Comput. Neurosci. 1, 89–107 (1994).

    Article  CAS  Google Scholar 

  29. Gold, J.I. & Shadlen, M.N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 5, 10–16 (2001).

    Article  Google Scholar 

  30. Salzman, C.D., Britten, K.H. & Newsome, W.T. Cortical microstimulation influences perceptual judgments of motion direction. Nature 346, 174–177 (1990).

    Article  CAS  Google Scholar 

  31. Groh, J.M., Born, R.T. & Newsome, W.T. How is a sensory map read out? Effects of microstimulation in visual area MT on saccades and smooth pursuit eye movements. J. Neurosci. 17, 4312–4330 (1997).

    Article  CAS  Google Scholar 

  32. Nichols, M.J. & Newsome, W.T. Middle temporal visual area microstimulation influences veridical judgments of motion direction. J. Neurosci. 22, 9530–9540 (2002).

    Article  CAS  Google Scholar 

  33. Sekuler, R. & Ball, K. Mental set alters visibility of moving targets. Science 32, 60–62 (1977).

    Article  Google Scholar 

  34. Smith, P.L. & Ratcliff, R. Psychology and neurobiology of simple decisions. Trends Neurosci. 27, 161–168 (2004).

    Article  CAS  Google Scholar 

  35. Roitman, J.D. & Shadlen, M.N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).

    Article  CAS  Google Scholar 

  36. Wang, X.J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).

    Article  CAS  Google Scholar 

  37. Koulakov, A.A., Raghavachari, S., Kepecs, A. & Lisman, J.E. Model for a robust neural integrator. Nat. Neurosci. 5, 775–782 (2002).

    Article  CAS  Google Scholar 

  38. Movshon, J.A., Adelson, E.H., Gizzi, M.S. & Newsome, W.T. The analysis of moving visual patterns. in Experimental Brain Research Supplementum II: Pattern Recognition Mechanisms (eds. Chagas, C., Gattass, R. & Gross, C.) 117–151 (Springer, New York, 1986).

    Google Scholar 

  39. Simoncelli, E.P. & Heeger, D.J. A model of neuronal responses in visual area MT. Vision Res. 38, 743–761 (1998).

    Article  CAS  Google Scholar 

  40. Mazurek, M.E., Roitman, J.D., Ditterich, J. & Shadlen, M.N. A role for neural integrators in perceptual decision making. Cereb. Cortex 13, 1257–1269 (2003).

    Article  Google Scholar 

  41. Snyder, L.H., Batista, A.P. & Andersen, R.A. Coding of intention in the posterior parietal cortex. Nature 386, 167–170 (1997).

    Article  CAS  Google Scholar 

  42. Shadlen, M.N. & Newsome, W.T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by a research grant from the US National Institutes of Health (EY2017). We thank P. Latham, B. Lau and E.P. Simoncelli for helpful advice and discussion.

Author information

Authors and Affiliations

  1. Center for Neural Science, 4 Washington Place, Room 809, New York University, New York, 10003, New York, USA

    Mehrdad Jazayeri & J Anthony Movshon

Authors
  1. Mehrdad Jazayeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J Anthony Movshon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J Anthony Movshon.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jazayeri, M., Movshon, J. Optimal representation of sensory information by neural populations. Nat Neurosci 9, 690–696 (2006). https://doi.org/10.1038/nn1691

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1691

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing