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Owl's behavior and neural representation predicted by Bayesian inference

Nature Neuroscience volume 14, pages 1061–1066 (2011)Cite this article

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Abstract

The owl captures prey using sound localization. In the classical model, the owl infers sound direction from the position of greatest activity in a brain map of auditory space. However, this model fails to describe the actual behavior. Although owls accurately localize sources near the center of gaze, they systematically underestimate peripheral source directions. We found that this behavior is predicted by statistical inference, formulated as a Bayesian model that emphasizes central directions. We propose that there is a bias in the neural coding of auditory space, which, at the expense of inducing errors in the periphery, achieves high behavioral accuracy at the ethologically relevant range. We found that the owl's map of auditory space decoded by a population vector is consistent with the behavioral model. Thus, a probabilistic model describes both how the map of auditory space supports behavior and why this representation is optimal.

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Figure 1: Models of the owl's behavior.
Figure 2: Predicted behavior under varying levels of interaural correlation.
Figure 3: Measured prior distribution of target direction.
Figure 4: Performance of alternative estimators.
Figure 5: Population vector approximation to Bayesian estimator.
Figure 6: Predicted midbrain representation of auditory space.

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References

  1. Knudsen, E.I., Blasdel, G.G. & Konishi, M. Sound localization by the barn owl (Tyto alba) measured with the search coil technique. J. Comp. Physiol. 133, 1–11 (1979).

    Article  Google Scholar 

  2. Hausmann, L., von Campenhausen, M., Endler, F., Singheiser, M. & Wagner, H. Improvements of sound localization abilities by the facial ruff of the barn owl (Tyto alba) as demonstrated by virtual ruff removal. PLoS ONE 4, e7721 (2009).

    Article  Google Scholar 

  3. Populin, L.C. & Yin, T.C. Behavioral studies of sound localization in the cat. J. Neurosci. 18, 2147–2160 (1998).

    Article  CAS  Google Scholar 

  4. Jay, M.F. & Sparks, D.L. Localization of auditory and visual targets for the initiation of saccadic eye movements. in Comparative Perception: Basic Mechanisms (eds. Berkley, M.A. & Stebbins, W.C.) 351–374 (Wiley, New York, 1990).

  5. Nodal, F.R., Bajo, V.M., Parsons, C.H., Schnupp, J.W. & King, A.J. Sound localization behavior in ferrets: comparison of acoustic orientation and approach-to-target responses. Neuroscience 154, 397–408 (2008).

    Article  CAS  Google Scholar 

  6. Zahn, J.R., Abel, L.A. & Dell'Osso, L.F. Audio-ocular response characteristics. Sens. Processes 2, 32–37 (1978).

    CAS  PubMed  Google Scholar 

  7. Blauert, J. Spatial Hearing: The Psychophysics of Human Sound Localization. (MIT Press, Cambridge, Massachusetts, 1983).

    Google Scholar 

  8. Carr, C.E. & Konishi, M. A circuit for detection of interaural time differences in the brain stem of the barn owl. J. Neurosci. 10, 3227–3246 (1990).

    Article  CAS  Google Scholar 

  9. Knudsen, E.I. & Konishi, M. A neural map of auditory space in the owl. Science 200, 795–797 (1978).

    Article  CAS  Google Scholar 

  10. Olsen, J.F., Knudsen, E.I. & Esterly, S.D. Neural maps of interaural time and intensity differences in the optic tectum of the barn owl. J. Neurosci. 9, 2591–2605 (1989).

    Article  CAS  Google Scholar 

  11. Masino, T. & Knudsen, E.I. Horizontal and vertical components of head movements are controlled by distinct neural circuits in the barn owl. Nature 345, 434–437 (1990).

    Article  CAS  Google Scholar 

  12. Jeffress, L.A. A place theory of sound localization. J. Comp. Physiol. Psychol. 41, 35–39 (1948).

    Article  CAS  Google Scholar 

  13. Konishi, M. Coding of auditory space. Annu. Rev. Neurosci. 26, 31–55 (2003).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Stocker, A.A. & Simoncelli, E.P. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci. 9, 578–585 (2006).

    Article  CAS  Google Scholar 

  16. Nix, J. & Hohmann, V. Sound source localization in real sound fields based on empirical statistics of interaural parameters. J. Acoust. Soc. Am. 119, 463–479 (2006).

    Article  Google Scholar 

  17. Christianson, G.B. & Peña, J.L. Noise reduction of coincidence detector output by the inferior colliculus of the barn owl. J. Neurosci. 26, 5948–5954 (2006).

    Article  CAS  Google Scholar 

  18. Pecka, M., Siveke, I., Grothe, B. & Lesica, N.A. Enhancement of ITD coding within the initial stages of the auditory pathway. J. Neurophysiol. 103, 38–46 (2010).

    Article  Google Scholar 

  19. Keller, C.H., Hartung, K. & Takahashi, T.T. Head-related transfer functions of the barn owl: measurement and neural responses. Hear. Res. 118, 13–34 (1998).

    Article  CAS  Google Scholar 

  20. Saberi, K. et al. Effects of interaural decorrelation on neural and behavioral detection of spatial cues. Neuron 21, 789–798 (1998).

    Article  CAS  Google Scholar 

  21. Fischer, B.J., Christianson, G.B. & Peña, J.L. Cross-correlation in the auditory coincidence detectors of the owl. J. Neurosci. 28, 8107–8115 (2008).

    Article  CAS  Google Scholar 

  22. Edut, S. & Eilam, D. Protean behavior under barn-owl attack: voles alternate between freezing and fleeing and spiny mice flee in alternating patterns. Behav. Brain Res. 155, 207–216 (2004).

    Article  Google Scholar 

  23. Smith, E.C. & Lewicki, M.S. Efficient auditory coding. Nature 439, 978–982 (2006).

    Article  CAS  Google Scholar 

  24. Georgopoulos, A.P., Kalaska, J.F., Crutcher, M.D., Caminiti, R. & Massey, J.T. The representation of movement direction in the motor cortex: single cell and population studies. in Dynamic Aspects of Neocortical Function (eds. Edelman, G.M., Goll, W.E. & Cowan, W.M.) 501–524 (Neurosciences Research Foundation, New York, 1984).

  25. van Hemmen, J.L. & Schwartz, A.B. Population vector code: a geometric universal as actuator. Biol. Cybern. 98, 509–518 (2008).

    Article  Google Scholar 

  26. Shi, L. & Griffiths, T.L. Neural implementation of hierarchical Bayesian inference by importance sampling. in Advances in Neural Information Processing Systems NIPS Vol. 22 (eds. Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I. & Culotta, A.) (MIT Press, Cambridge, Massachusetts, 2009).

  27. Pérez, M.L., Shanbhag, S.J. & Peña, J.L. Auditory spatial tuning at the crossroads of the midbrain and forebrain. J. Neurophysiol. 102, 1472–1482 (2009).

    Article  Google Scholar 

  28. Knudsen, E.I. Auditory and visual maps of space in the optic tectum of the owl. J. Neurosci. 2, 1177–1194 (1982).

    Article  CAS  Google Scholar 

  29. Harper, N.S. & McAlpine, D. Optimal neural population coding of an auditory spatial cue. Nature 430, 682–686 (2004).

    Article  CAS  Google Scholar 

  30. Stern, R.M. & Trahiotis, C. Models of binaural perception. in Binaural and Spatial Hearing in Real and Virtual Environments (eds. Gilkey, R. & Anderson, T.R.) 499–531 (Lawrence Erlbaum Associates, New York, USA, 1996).

  31. Bergan, J.F., Ro, P., Ro, D. & Knudsen, E.I. Hunting increases adaptive auditory map plasticity in adult barn owls. J. Neurosci. 25, 9816–9820 (2005).

    Article  CAS  Google Scholar 

  32. Simoncelli, E.P. Optimal estimation in sensory systems. In The Cognitive Neurosciences, IV (ed. Gazzaniga, M.) 525–535 (MIT Press, Cambridge, Massachusetts, 2009).

  33. Ganguli, D. & Simoncelli, E.P. Implicit encoding of prior probabilities in optimal neural populations. in Advances in Neural Information Processing Systems NIPS Vol. 23 (eds. Lafferty, J., Williams, C.K.I., Shawe-Taylor, J., Zemel R.S. & Culotta, A.) (MIT Press, Cambridge, Massachusetts, 2010).

  34. Seung, H.S. & Sompolinsky, H. Simple models for reading neural population codes. Proc. Natl. Acad. Sci. USA 93, 339–344 (1993).

    Google Scholar 

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

    Article  CAS  Google Scholar 

  36. Köver, H. & Bao, S. Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception. PLoS ONE 5, e10497 (2010).

    Article  Google Scholar 

  37. Girshick, A.R., Landy, M.S. & Simoncelli, E.P. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat. Neurosci. advance online publication, doi:10.1038/nn.2831 (5 June 2011).

  38. Li, B., Patterson, M.R. & Freeman, R.D. Oblique effect: a neural basis in the visual cortex. J. Neurophysiol. 90, 204–217 (2003).

    Article  Google Scholar 

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Acknowledgements

We thank L. Hausmann and H. Wagner for providing the behavioral data and C. Keller and T. Takahashi for providing the head-related transfer functions. We thank S. Edut, D. Eilam, E. Knudsen, M. Konishi and K. Saberi, whose work substantially contributed to testing our hypothesis. We thank S. Deneve, M. Konishi, A. Margolis, A. Oster, O. Schwartz and A. Wohrer for advice and comments on the manuscript. This study was supported by US National Institutes of Health grant DC007690 (J.L.P.) and the Marie Curie Team of Excellence grant BIND MECT-CT-20095-02481 (B.J.F.).

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Authors and Affiliations

  1. Département d'Etudes Cognitives, Group for Neural Theory, Ecole Normale Supérieure, Paris, France

    Brian J Fischer

  2. Laboratoire de Neurosciences Cognitives, INSERM U960, Paris, France

    Brian J Fischer

  3. Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx,, New York, USA

    José Luis Peña

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Contributions

B.J.F. designed the model and performed the model simulations. J.L.P. supervised the project. B.J.F. and J.L.P. wrote the paper.

Corresponding author

Correspondence to Brian J Fischer.

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The authors declare no competing financial interests.

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Fischer, B., Peña, J. Owl's behavior and neural representation predicted by Bayesian inference. Nat Neurosci 14, 1061–1066 (2011). https://doi.org/10.1038/nn.2872

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