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Retinal input instructs alignment of visual topographic maps.

ABSTRACT: Sensory information is represented in the brain in the form of topographic maps, in which neighboring neurons respond to adjacent external stimuli. In the visual system, the superior colliculus receives topographic projections from the retina and primary visual cortex (V1) that are aligned. Alignment may be achieved through the use of a gradient of shared axon guidance molecules, or through a retinal-matching mechanism in which axons that monitor identical regions of visual space align. To distinguish between these possibilities, we take advantage of genetically engineered mice that we show have a duplicated functional retinocollicular map but only a single map in V1. Anatomical tracing revealed that the corticocollicular projection bifurcates to align with the duplicated retinocollicular map in a manner dependent on the normal pattern of spontaneous activity during development. These data suggest a general model in which convergent maps use coincident activity patterns to achieve alignment.

SUBMITTER: Triplett JW

PROVIDER: S-EPMC2814139 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Retinal input instructs alignment of visual topographic maps.

Triplett Jason W JW Owens Melinda T MT Yamada Jena J Lemke Greg G Cang Jianhua J Stryker Michael P MP Feldheim David A DA

Cell 20091001 1

Sensory information is represented in the brain in the form of topographic maps, in which neighboring neurons respond to adjacent external stimuli. In the visual system, the superior colliculus receives topographic projections from the retina and primary visual cortex (V1) that are aligned. Alignment may be achieved through the use of a gradient of shared axon guidance molecules, or through a retinal-matching mechanism in which axons that monitor identical regions of visual space align. To disti ...[more]

PMID: 19804762

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Project description:Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed 'by eye'. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation 'respects' the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the 'noise' caused by artefacts and permits a clearer representation of the dominant, 'real' distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome.

Dataset Information

Retinal input instructs alignment of visual topographic maps.

Publications

Retinal input instructs alignment of visual topographic maps.

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