Skip to main content

Neural Mechanisms for Mid-Level Optical Flow Pattern Detection

  • Conference paper
Artificial Neural Networks – ICANN 2007 (ICANN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4669))

Included in the following conference series:


This paper describes a new model for extracting large-field optical flow patterns to generate distributed representations of neural activation to control complex visual tasks such as 3D egomotion. The neural mechanisms draw upon experimental findings about the response properties and specificities of cells in areas V1, MT and MSTd along the dorsal pathway. Model V1 cells detect local motion estimates. Model MT cells in different pools are suggested to be selective to motion patterns integrating from V1 as well as to velocity gradients. Model MSTd cells considered here integrate MT gradient cells over a much larger spatial neighborhood to generate the observed pattern selectivity for expansion/contraction, rotation and spiral motion, providing the necessary input for spatial navigation mechanisms. Our model also incorporates feedback processing between areas V1-MT and MT-MSTd. We demonstrate that such a re-entry of context-related information helps to disambiguate and stabilize more localized processing along the primary motion pathway.

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

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Albright, T.D.: Direction and orientation selectivity of neurons in visual area MT of the macaque. J. Neurophysiol. 52, 1106–1130 (1984)

    Google Scholar 

  2. Bayerl, P., Neumann, H.: Disambiguating visual motion through contextual feedback modulation. Neural Comp. 16, 2041–2066 (2004)

    Article  MATH  Google Scholar 

  3. Bayerl, P., Neumann, H.: A fast biologically inspired algorithm for recurrent motion estimation. IEEE Trans. on PAMI 29, 246–260 (2007)

    Google Scholar 

  4. Beardsley, S.A., Vaina, L.M.: Computational modeling of optical flow sensitivity in MSTd neurons. Comp. Neural Syst. 9, 467–493 (1998)

    Article  MATH  Google Scholar 

  5. Ben-Shahar, O., Zucker, S.: Geometrical computations explain projection patterns of long-range horizontal connections in visual cortex. Neural Comp. 16, 445–476 (2004)

    Article  MATH  Google Scholar 

  6. Born, R.T., Bradley, D.C.: Structure and function of visual area MT. Ann. Rev. Neurosci. 28, 157–189 (2005)

    Article  Google Scholar 

  7. Crick, F., Koch, C.: Constraints on cortical and thalamic projections: The no-strong loops hypothesis. Nature 391, 245–250 (1998)

    Article  Google Scholar 

  8. Duffy, C.J., Wurtz, R.H.: Sensitivity of MST neurons to optic flow stimuli I. A continuum of response selectivity to large-field stimuli. Neurophysiol 65, 1329–1345 (1991)

    Google Scholar 

  9. Gibson, J.J.: The Ecological Approach to Visual Perception. LEA, Hillsdale, NJ (1986)

    Google Scholar 

  10. Graziano, M.S.A., Anderson, R.A., Snowden, R.: Tuning of MST neurons to spiral motions. J. Neurosci. 14, 54–67 (1994)

    Google Scholar 

  11. Grossberg, S., Mingolla, E., Pack, C.: A neural model of motion processing and visual navigation by cortical area MST. Cerebral Cortex 9, 878–895 (1999)

    Article  Google Scholar 

  12. Herz, A.V.M., Gollisch, T., Machens, C.K., Jaeger, D.: Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science 314, 80–85 (2006)

    Article  MathSciNet  Google Scholar 

  13. Pack, C.C., Born, R.T.: Temporal dynamics of a neural solution to the aperture problem in cortical area MT. Nature 409, 1040–1042 (2001)

    Article  Google Scholar 

  14. Thielscher, A., Neumann, H.: Neural mechanisms of cortico-cortical interaction in texture boundary detection: A modeling approach. Neuroscience 122, 921–939 (2003)

    Article  Google Scholar 

  15. Thorpe, S., Delorme, A., Van Rullen, R.: Spike-based strategies for rapid processing. Neural Networks 14, 715–726 (2001)

    Article  Google Scholar 

  16. Treue, S., Anderson, R.A.: Neural responses to velocity gradients in macaque cortical area MT. Vis. Neurosci. 13, 797–804 (1996)

    Article  Google Scholar 

  17. Tsotsos, J.K., Liu, Y., Martinez-Trujillo, J.C., Pomplun, M., Simine, E., Zhou, K.: Attending to visual motion. Computer Vision and Image Understanding 100, 3–40 (2005)

    Article  Google Scholar 

  18. Xiao, D.-K., Raiguel, S., Marcar, V., Orban, G.: The spatial distribution of the antagonistic surround of MT/V5 neurons. Cerebral Cortex 7, 662–677 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ringbauer, S., Bayerl, P., Neumann, H. (2007). Neural Mechanisms for Mid-Level Optical Flow Pattern Detection. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74693-5

  • Online ISBN: 978-3-540-74695-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics