IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v284y2000i1p318-334.html
   My bibliography  Save this article

A wire length minimization approach to ocular dominance patterns in mammalian visual cortex

Author

Listed:
  • Chklovskii, Dmitri B
  • Koulakov, Alexei A

Abstract

The primary visual area (V1) of the mammalian brain is a thin sheet of neurons. Because each neuron is dominated by either right or left eye one can treat V1 as a binary mixture of neurons. The spatial arrangement of neurons dominated by different eyes is known as the ocular dominance (OD) pattern. We propose a theory for OD patterns based on the premise that they are evolutionary adaptations to minimize the length of intra-cortical connections. Thus, the existing OD patterns are obtained by solving a wire length minimization problem. We divide all the neurons into two classes: right- and left-eye dominated. We find that if the number of connections of each neuron with the neurons of the same class differs from that with the other class, the segregation of neurons into monocular regions indeed reduces the wire length. The shape of the regions depends on the relative number of neurons in the two classes. If both classes are equally represented we find that the optimal OD pattern consists of alternating stripes. If one class is less numerous than the other, the optimal OD pattern consists of patches of the underrepresented (ipsilateral) eye dominated neurons surrounded by the neurons of the other class. We predict the transition from stripes to patches when the fraction of neurons dominated by the ipsilateral eye is about 40%. This prediction agrees with the data in macaque and Cebus monkeys. Our theory can be applied to other binary cortical systems.

Suggested Citation

  • Chklovskii, Dmitri B & Koulakov, Alexei A, 2000. "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 284(1), pages 318-334.
  • Handle: RePEc:eee:phsmap:v:284:y:2000:i:1:p:318-334
    DOI: 10.1016/S0378-4371(00)00219-3
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437100002193
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/S0378-4371(00)00219-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Robert Legenstein & Niko Wilbert & Laurenz Wiskott, 2010. "Reinforcement Learning on Slow Features of High-Dimensional Input Streams," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-13, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:284:y:2000:i:1:p:318-334. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.