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      Specificity of Neuronal Responses in Primary Visual Cortex Is Modulated by Interhemispheric CorticoCortical Input

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          Abstract

          Within the visual cortex, it has been proposed that interhemispheric interactions serve to re-establish the continuity of the visual field across its vertical meridian (VM) by mechanisms similar to those used by intrinsic connections within a hemisphere. However, other specific functions of transcallosal projections have also been proposed, including contributing to disparity tuning and depth perception. Here, we consider whether interhemispheric connections modulate specific response properties, orientation and direction selectivity, of neurons in areas 17 and 18 of the ferret by combining reversible thermal deactivation in one hemisphere with optical imaging of intrinsic signals and single-cell electrophysiology in the other hemisphere. We found interhemispheric influences on both the strength and specificity of the responses to stimulus orientation and direction of motion, predominantly at the VM. However, neurons and domains preferring cardinal contours, in particular vertical contours, seem to receive stronger interhemispheric input than others. This finding is compatible with interhemispheric connections being involved in horizontal disparity tuning. In conclusion, our results support the view that interhemispheric interactions mainly perform integrative functions similar to those of connections intrinsic to one hemisphere.

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          Exuberance in the development of cortical networks.

          The cerebral cortex is the largest and most intricately connected part of the mammalian brain. Its size and complexity has increased during the course of evolution, allowing improvements in old functions and causing the emergence of new ones, such as language. This has expanded the behavioural and cognitive repertoire of different species and has determined their competitive success. To allow the relatively rapid emergence of large evolutionary changes in a structure of such importance and complexity, the mechanisms by which cortical circuitry develops must be flexible and yet robust against changes that could disrupt the normal functions of the networks.
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            Axon overproduction and elimination in the corpus callosum of the developing rhesus monkey.

            We have studied the cytological and quantitative aspects of axon addition and elimination in the corpus callosum of the developing rhesus monkey. Electron microscopic analysis reveals that during fetal development the number of callosal axons increases from 4 million at embryonic day 65 (E65) to 188 million at birth (E 165). Thus, the number of callosal axons in newborn monkeys exceeds the number present in the adult (an average of 56 million; LaMantia and Rakic, 1990a) by at least 3.5 times. Although there is some variability among the 11 fetal and newborn monkeys examined, there appears to be a progressive increase in the total number of callosal axons from midgestation through birth. The presence and numbers of growth cones from E65 through birth suggests that axon addition occurs exclusively during this period. There is no ultrastructural or quantitative indication of postnatal axon addition. After birth, about 70% of the axons in the callosum are eliminated in 2 phases. During the first phase, which includes the first 3 postnatal weeks, approximately 80 million axons are lost at an estimated rate of 4.4 million/d or 50/sec. During the second phase, which continues for the following 3 months, an additional 50 million axons are eliminated at a rate of 0.5 million/d or 5/sec until the adult value is reached. A discontinuous distribution of different classes of axons along the anterior-posterior axis of the tract reminiscent of the pattern seen in the adult is detectable before the onset of the first phase of axon elimination. Since the basic topography and terminal field patterns of callosal projections are well established before birth in all regions of the monkey cortex examined so far (Goldman-Rakic et al., 1983; Killackey and Chalupa, 1986; Dehay et al., 1988; Schwartz and Goldman-Rakic, 1990), we conclude that the massive postnatal elimination of callosal axons described here is unlikely to play a significant role in the development of discretely patterned callosal projection zones or their columnar terminations. The coincidence of axon elimination and the increase in synaptic density throughout the primate cerebral cortex during the first 6 postnatal months (Rakic et al., 1986), however, suggests that supernumerary axons may be lost during a process that results in the local proliferation of synapses from a subset of initial interhemispheric projections.
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              Orientation tuning curves: empirical description and estimation of parameters.

              This paper compares the ability of some simple model functions to describe orientation tuning curves obtained in extracellular single-unit recordings from area 17 of the cat visual cortex. It also investigates the relationships between three methods currently used to estimate preferred orientation from tuning curve data: (a) least-squares curve fitting, (b) the vector sum method and (c) the Fourier transform method (Wörgötter and Eysel 1987). The results show that the best fitting model function for single-unit orientation tuning curves is a von Mises circular function with a variable degree of skewness. However, other functions, such as a wrapped Gaussian, fit the data nearly as well. A cosine function provides a poor description of tuning curves in almost all instances. It is demonstrated that the vector sum and Fourier methods of determining preferred orientation are equivalent, and identical to calculating a least-square fit of a cosine function to the data. Least-squares fitting of a better model function, such as a von Mises function or a wrapped Gaussian, is therefore likely to be a better method for estimating preferred orientation. Monte-Carlo simulations confirmed this, although for broad orientation tuning curves sampled at 45 degree intervals, as is typical in optical recording experiments, all the methods gave similarly accurate estimates of preferred orientation. The sampling interval, the estimated error in the response measurements and the probable shape of the underlying response function all need to be taken into account in deciding on the best method of estimating referred orientation from physiological measurements of orientation tuning data.
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                Author and article information

                Journal
                Cereb Cortex
                cercor
                cercor
                Cerebral Cortex (New York, NY)
                Oxford University Press
                1047-3211
                1460-2199
                December 2010
                08 March 2010
                08 March 2010
                : 20
                : 12
                : 2776-2786
                Affiliations
                [1 ]Max-Planck Research Group: Cortical Function and Dynamics, Max Planck Institute for Brain Research, 60528 Frankfurt/Main, Germany
                [2 ]Centre for Brain and Mind, Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario N6A 5C1, Canada
                [3 ]Division of Neuroanatomy and Brain Development, Department of Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden
                Author notes
                Address correspondence to Dr Kerstin E. Schmidt, Max-Planck Research Group: Cortical Function and Dynamics, Max Planck Institute for Brain Research, Deutschordenstraße 46, 60528 Frankfurt/Main, Germany. Email: schmidt@ 123456mpih-frankfurt.mpg.de .
                Article
                10.1093/cercor/bhq024
                2978237
                20211943
                ec3124d5-e77a-4b39-b3e0-b3dd72286a19
                © The Authors 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Categories
                Articles

                Neurology
                corpus callosum,ferret,orientation selectivity,cooling deactivation,optical imaging
                Neurology
                corpus callosum, ferret, orientation selectivity, cooling deactivation, optical imaging

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