full reference for Nisbet et al.

Cutting, Hunter O.

doi:10.5194/esd-2020-16-sc6

Cited by 2 publications

(3 citation statements)

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“…To obtain the LSC in natural images transformed with the eye optics of a human eye, we defined a generic receptive field as a 2D gaussian. The size of the receptive fields was chosen based on measurements in human retina (71). The receptive field of human parasol cells ranges roughly from 100 to 200 µm, with a median at 144 µm for ON cells and 123 µm for OFF cells.…”

Section: Mean Intensity and Local Spatial Contrast In Human Retinal I...mentioning

confidence: 99%

Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes

Goethals,

Louboutin,

Hamlaoui

et al. 2024

Preprint

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Eye growth is regulated by the visual input. Many studies suggest that the retina can detect if a visual image is focused in front or behind the back of the eye, and modulate eye growth to bring it back to focus. How can the retina distinguish between these two types of defocus? Here we simulated how eye optics transform natural images and recorded how the isolated retina responds to different types of simulated defocus. We found that some ganglion cell types could distinguish between an image focussed in front or behind the retina, by estimating spatial contrast. Aberrations in the eye optics made spatial contrast, but not luminance, a reliable cue to distinguish these two types of defocus. Our results suggest a mechanism for how the retina can estimate the sign of defocus and provide an explanation for several results aiming at mitigating strong myopia by slowing down eye growth.

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Section: Mean Intensity and Local Spatial Contrast In Human Retinal I...mentioning

confidence: 99%

Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes

Goethals,

Louboutin,

Hamlaoui

et al. 2024

Preprint

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“…Here, we utilised hierarchical ontologies of brain regions, a formal representation of knowledge about the species-specific brain anatomy [55] (Figure 1), i.e., which brain regions it consists of and how these brain regions are subdivided (hierarchically). The Allen Institute provides ontologies for both mouse and human [28,56,57] that include a mapping onto our respect reference spaces, i.e., which coordinate of the reference space belongs to which brain region in the ontology. Since datasets are not necessarily annotated with the same ontology and on the same hierarchical level, they cannot be compared across anatomical scales and resolution directly.…”

Section: Mapping To a Common Reference Spacementioning

confidence: 99%

“…Imaging data that is shown in this paper, namely in-situ hybridization data [24], axonal projection connectivity [23] and resting state functional connectivity [7] was already aligned to the reference spaces used for this resource [28,56,57]. Novel datasets could be aligned via tools such as the QUINT workflow [85] or the ANTS frame work [86].…”

Section: Data Mapping and Queryingmentioning

confidence: 99%

BrainTACO: An Explorable Multi-Scale Multi-Modal Brain Transcriptomic And Connectivity Data Resource

Ganglberger

Toepfer

Kargl

et al. 2023

Preprint

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Exploring the relationships between genes, brain circuitry, and behaviour is accelerated by the joint analysis of a heterogeneous sets form 3D imaging data, anatomical data, and brain networks at varying scales, resolutions, and modalities. Hence, generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources for gene expression dissection of brain networks with an unprecedented coverage. Using this platform, allows to straightforward explore and extract brain data for identifying potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script based toolboxes, and supports neuroscientists by focusing on leveraging the data instead of preparing it.

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full reference for Nisbet et al.

Cited by 2 publications

References 0 publications

Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes

Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes

BrainTACO: An Explorable Multi-Scale Multi-Modal Brain Transcriptomic And Connectivity Data Resource

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