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Modeling Brain Circuitry using Scales Ranging from Micrometer to Nanometer

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Furthermore, given the many recent advances in genet... ver más

31/03/2016

EPFL

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-03-31 No tenemos la información de la convocatoria

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1 Participantes

EPFL

0.00€ | Lider

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Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Furthermore, given the many recent advances in genetic engineering, viral targeting, and immunohistochemical labeling of specific cellular structures, there is a growing need for automated quantitative assessment of neuron morphology and connectivity. Electron microscopes can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the arborescence and the connections are integral parts of the brain's wiring diagram, combining these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. We will therefore use our Computer Vision expertise to provide not only the necessary tools to process images acquired using a specific modality but also those required to create an integrated representation using all available modalities. This is a radical departure from earlier approaches to applying Computer Vision techniques in this field, which have tended to focus on narrow problems. State-of-the-art methods have not reached the level of reliability and integration that would allow automated processing and interpretation of the massive amounts of data that are required for a true leap of our understanding of how the brain works. In other words, we cannot yet exploit the full potential of our imaging technology and that is what we intend to change.

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