Cytochrome-oxidase blobs are central to two of the most influential ideas in contemporary visual neuroscience--cortical modularity and parallel processing pathways. In particular, the regular 2D array of cytochrome-oxidase-rich blobs in primate visual cortex is arguably the most compelling evidence for cortical modularity and has been hypothesized to mark a separate processing stream through the visual cortex. Although previously a variety of mammals have been studied, blobs have only been demonstrated in the visual cortex of primates, which has led to the conclusion that blobs represent a primate-specific feature of visual cortical organization. Here we demonstrate the presence of cytochrome-oxidase blobs in a nonprimate species. Throughout the full tangential extent of layers II-III in cat visual cortex the cytochrome-oxidase staining pattern is distinctly patchy, with the darkly stained blobs forming a regular 2D array. In addition, the blobs in cat visual cortex are functionally related to the underlying ocular dominance columns. The presence of cytochrome-oxidase blobs in the cat clearly demonstrates that they no longer can be considered a primate-specific feature of visual cortical organization.

In layer IV of the primary visual cortex, in both the macaque monkey and the cat, geniculocortical terminals representing the two eyes are segregated into alternating zones known as ocular dominance bands. Viewed tangentially, in the monkey these bands take the form of a series of branching parallel stripes that run roughly perpendicular to the border of striate cortex. In the cat, the overall ocular dominance pattern consists of irregularly branching, beaded bands that exhibit no predominant orientation. If the striking differences in the appearance of these two patterns reflect important differences in the basic rules governing cortical ocular dominance, then this poses a problem for attempts to formulate general principles of visual cortical organization. However, it has been suggested that the differences in the appearance of the ocular dominance patterns in these two species could result simply from known differences in the boundary conditions of their geniculocortical pathways. This article describes the formulation and testing of a single computational model that accurately predicts the quite dissimilar ocular dominance patterns in cats and monkeys. This model also generalizes to predict the different ocular dominance patterns observed in young and old three-eyed frogs, supporting the notion that the overall pattern of ocular dominance is governed by a common set of rules. The significance of these results is discussed in terms of previous models, which have focused largely on local processes underlying the development of ocular dominance segregation. Although the present model is not a developmental one, it does shed some light on potential mechanisms for establishing retinotopy in striate cortex and on possible developmental relationships between the geniculostriate pathway and intrinsic modularity of the striate cortex.

New techniques for quantifying large numbers of proteins or genes are transforming the study of plasticity mechanisms in visual cortex (V1) into the era of big data. With those changes comes the challenge of applying new analytical methods designed for high-dimensional data.Studies of V1, however, can take advantage of the known functions that many proteins have in regulating experience-dependent plasticity to facilitate linking big data analyses with neurobiological functions. Here we discuss two workflows and provide example R code for analyzing high-dimensional changes in a group of proteins (or genes) using two data sets. The first data set includes 7 neural proteins, 9 visual conditions, and 3 regions in V1 from an animal model for amblyopia. The second data set includes 23 neural proteins and 31 ages (20d-80yrs) from human post-mortem samples of V1. Each data set presents different challenges and we describe using PCA, tSNE, and various clustering algorithms including sparse high-dimensional clustering. Also, we describe a new approach for identifying high-dimensional features and using them to construct a plasticity phenotype that identifies neurobiological differences among clusters. We include an R package "v1hdexplorer" that aggregates the various coding packages and custom visualization scripts written in R Studio.

Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1) and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologically meaningful results about visual plasticity from data-driven analytical methods designed for high-dimensional data. In other areas of neuroscience, high-information content methodologies are revealing more subtle aspects of neural development and individual variations that give rise to a richer picture of brain disorders. We have developed an approach for studying V1 plasticity that takes advantage of the known functions of many synaptic proteins for regulating visual plasticity and using that to rebrand the results of high-dimensional analyses into a plasticity phenotype. Here we provide a primer for analyzing experience-dependent plasticity in V1 using example R code to identify high-dimensional changes in a group of proteins. We describe using PCA to classify high-dimensional plasticity features and use them to construct a plasticity phenotype. In the examples, we show how the plasticity phenotype can be visualized and used to identify neurobiological features in V1 that change during development or after different visual rearing conditions. We include an R package "v1hdexplorer" that aggregates the various coding packages and custom visualization scripts written in R Studio.