Skip to main content

An Active Efficient Coding Model of Binocular Vision Development Under Normal and Abnormal Rearing Conditions

  • Conference paper
  • First Online:
From Animals to Animats 15 (SAB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10994))

Included in the following conference series:

  • 886 Accesses

Abstract

The development of binocular vision encompasses the formation of binocular receptive fields tuned to different disparities and the calibration of accurate vergence eye movements. Experiments have shown that this development is impaired when the animal is exposed to certain abnormal rearing conditions such as growing up in an environment that is deprived of horizontal or vertical edges. Here we test the effect of abnormal rearing conditions on a recently proposed computational model of binocular development. The model is formulated in the Active Efficient Coding framework, a generalization of classic efficient coding ideas to active perception. We show that abnormal rearing conditions lead to differences in the model’s development that qualitatively match those seen in animal experiments. Furthermore, the model predicts systematic changes in vergence accuracy due to abnormal rearing. We discuss implications of the model for the treatment of developmental disorders of binocular vision such as amblyopia and strabismus.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Barlow, H.B.: Possible principles underlying the transformations of sensory messages. Sensory Communication (1961)

    Google Scholar 

  2. Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision. Res. 37(23), 3311–3325 (1997)

    Article  Google Scholar 

  3. Zhao, Y., Rothkopf, C.A., Triesch, J., Shi, B.E.: A unified model of the joint development of disparity selectivity and vergence control. In: IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp. 1–6 (2012)

    Google Scholar 

  4. Teulière, C., Forestier, S., Lonini, L., Zhang, C., Zhao, Y., Shi, B.E., Triesch, J.: Self-calibrating smooth pursuit through active efficient coding. Robot. Auton. Syst. 71, 3–12 (2015)

    Article  Google Scholar 

  5. Lonini, L., Zhao, Y., Chandrashekhariah, P., Shi, B.E., Triesch, J.: Autonomous learning of active multi-scale binocular vision. In: IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp. 1–6 (2013)

    Google Scholar 

  6. Zhang, C., Triesch, J., Shi, B.E.: An active-efficient-coding model of optokinetic nystagmus. J. Vision 16(14), 10–10 (2016)

    Article  Google Scholar 

  7. Triesch, J., Eckmann, S., Shi, B.E.: A computational model for the joint development of accommodation and vergence control. J. Vision 17(10), 162–162 (2017)

    Article  Google Scholar 

  8. Vikram, T., Teulière, C., Zhang, C., Shi, B.E., Triesch, J.: Autonomous learning of smooth pursuit and vergence through active efficient coding. In: IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) (2014)

    Google Scholar 

  9. Chandrapala, T.N., Shi, B.E., Triesch, J.: On the utility of sparse neural representations in adaptive behaving agents. In: International Joint Conference on Neural Networks (IJCNN) (2015)

    Google Scholar 

  10. Lonini, L., Forestier, S., Teulière, C., Zhao, Y., Shi, B.E., Triesch, J.: Robust active binocular vision through intrinsically motivated learning. Front. Neurorobotics 7, 20–20 (2013)

    Article  Google Scholar 

  11. Freeman, R., Pettigrew, J.: Alteration of visual cortex from environmental asymmetries. J. Nature 246, 359–360 (1973)

    Article  Google Scholar 

  12. Tanaka, S., Ribot, J., Imamura, K., Tani, T.: Orientation-restricted continuous visual exposure induces marked reorganization of orientation maps in early life. Neuroimage 30, 462477 (2006)

    Article  Google Scholar 

  13. Tanaka, S., Tani, T., Ribot, J., OHashi, K., Imamura, K.: A postnatal critical period for orientation plasticity in the cat visual cortex. PLoS ONE 4, e5380 (2009)

    Article  Google Scholar 

  14. Hirsch, H.V.B., Spinelli, D.N.: Visual experience modifies distribution of horizontally and vertically oriented receptive fields in cats. Science 168, 869–871 (1970)

    Article  Google Scholar 

  15. Wiesel, T.N., Hubel, D.H.: Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26, 10031017 (1963)

    Google Scholar 

  16. Hunt, J.J., Dayan, P., Goodhill, G.J.: Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual input. PLoS Comput. Biol. 9(5), e1003005 (2013)

    Article  Google Scholar 

  17. Klimmasch, L., Lelais, A., Lichtenstein, A., Shi, B.E., Triesch, J.: Learning of active binocular vision in a biomechanical model of the oculomotor system. bioRxiv 160721 (2017). https://doi.org/10.1101/160721

  18. Priamikov, A., Fronius, M., Shi, B.E., Triesch, J.: OpenEyeSim: a biomechanical model for simulation of closed-loop visual perception. J. Vision 16(15), 25–25 (2016)

    Article  Google Scholar 

  19. Olmos, A., Kingdom, F.A.: A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33(12), 1463–1473 (2004)

    Article  Google Scholar 

  20. Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41, 3397–3415 (1993)

    Article  Google Scholar 

  21. Albert, M.V., Schnabel, A., Field, D.J.: Innate visual learning through spontaneous activity patterns. PLoS Comput. Biol. 4(8), e1000137 (2008)

    Article  Google Scholar 

  22. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  23. Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge (2005)

    Google Scholar 

  24. Van Hasselt, H., Wiering, M.A.: Reinforcement learning in continuous action spaces. In: IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, pp. 272–279 (2007)

    Google Scholar 

  25. Chandrapala, T.N., Shi, B.E., Triesch, J.: Active maintenance of binocular correspondence leads to orientation alignment of visual receptive fields. In: Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp. 98–103 (2015)

    Google Scholar 

  26. Appelle, S.: Perception and discrimination as a function of stimulus orientation: the “oblique effect” in man and animals. Psychol. Bull. 78(4), 266–278 (1972)

    Article  Google Scholar 

  27. Priamikov, A., Narayan, V., Shi, B.E., Triesch, J.: The role of contrast sensitivity in the development of binocular vision: A computational study. In: Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp. 33–38 (2015)

    Google Scholar 

  28. Leventhal, A.G., Hirsch, H.V.: Cortical effect of early selective exposure to diagonal lines. Science 190(4217), S.902–S.904 (1975)

    Article  Google Scholar 

  29. Stryker, M.P., Sherk, H., Leventhal, A.G., Hirsch, H.V.: Physiological consequences for the cat’s visual cortex of effectively restricting early visual experience with oriented contours. J. Neurophysiology 41(4), 896–909 (1978)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the German Federal Ministry of Education and Research under Grants 01GQ1414 and 01EW1603A, the European Union’s Horizon 2020 Grant 713010, the Hong Kong Research Grants Council under Grant 16244416, and the Quandt Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lukas Klimmasch .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Klimmasch, L., Schneider, J., Lelais, A., Shi, B.E., Triesch, J. (2018). An Active Efficient Coding Model of Binocular Vision Development Under Normal and Abnormal Rearing Conditions. In: Manoonpong, P., Larsen, J., Xiong, X., Hallam, J., Triesch, J. (eds) From Animals to Animats 15. SAB 2018. Lecture Notes in Computer Science(), vol 10994. Springer, Cham. https://doi.org/10.1007/978-3-319-97628-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97628-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97627-3

  • Online ISBN: 978-3-319-97628-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics