1999/06/14 by Dmitri B. Chklovskii, Chklovskii, Dmitri B., Alexei A. Koulakov +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · Physics and Astronomy · #Biological Physics (physics.bio-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Quantitative Biology (q-bio) #Retinal Development and Disorders #Soft Condensed Matter (cond-mat.soft) #Visual perception and processing mechanisms #cond-mat.dis-nn #cond-mat.soft #cs.NE #physics.bio-ph #q-bio
paper · pdf · doi:10.48550/arxiv.cond-mat/9906206
9 pages, submitted to Journal of Neuroscience
arxiv created 1999/06/14 · openalex publication_date 1999/06/14 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a theory for ocular dominance (OD) patterns in mammalian primary visual cortex. This theory is based on the premise that OD pattern is an adaptation to minimize the length of intra-cortical wiring. Thus we can understand the existing OD patterns by solving a wire length minimization problem. We divide all the neurons into two classes: left-eye dominated and right-eye dominated. We find that segregation of neurons into monocular regions reduces wire length if the number of connections with the neurons of the same class differs from that with the other class. The shape of the regions depends on the relative fraction of neurons in the two classes. If the numbers are close we find that the optimal OD pattern consists of interdigitating stripes. If one class is less numerous than the other, the optimal OD pattern consists of patches of the first class neurons in the sea of the other class neurons. 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. This theory can be applied to other binary cortical systems.