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NEUROGAIN

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Nonlinear synaptic integration in morphologically simple and complex neurons

Understanding how neurons represent and transform information and perform the computations required to generate complex behaviours is a core aim of neuroscience. Although this knowledge is of fundamental importance and is a prereq... ver más

28/02/2018

UNIVERSITY COLLEGE...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITY COLLEGE LONDON No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2018-02-28 No tenemos la información de la convocatoria

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1 Participantes

UNIVERSITY COLLEGE LONDON

2.50M€ | Lider

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Líder del proyecto

UNIVERSITY COLLEGE LONDON No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Understanding how neurons represent and transform information and perform the computations required to generate complex behaviours is a core aim of neuroscience. Although this knowledge is of fundamental importance and is a prerequisite for understanding many neuronal disorders, the way in which individual neurons process information is poorly understood. This reflects the technical difficulty of measuring rapid synaptic signalling in fine dendritic structures distributed in 3D space. Moreover, defining the transformation between inputs and output firing is complex due to the large number of input combinations involved. To overcome these problems I have recently developed two powerful new tools. A high speed 3D 2-photon acousto-optic lens (AOL) microscope that enables high speed (30 kHz) 3D optical functional imaging with submicron resolution. I will use AOL microscopy with uncaging, electrophysiological and optogenetic approaches to study synaptic integration in cerebellar granule cells, cortical pyramidal cells and fast-spiking interneurons. Comparison of synaptic integration and neuronal computation in these neurons, which include simple and complex morphologies and excitatory and inhibitory cell types, will allow mechanisms to be investigated and principles identified. By measuring [Ca2+] and voltage at many locations within 3D dendritic trees, I will map the spatiotemporal properties of synaptic input in 3D and locate and count regenerative events. By applying AOL microscopy, in vitro and in vivo I will elucidate mechanisms and test how information is processed by neurons during sensory input. I have also developed neuroConstruct, a software tool for modelling neurons and large-scale networks in 3D with a high degree of biological detail. By using in vitro and in vivo data to constrain models we will bridge the gap in our understanding between synaptic and neuronal mechanisms and the input-output transformations that morphologically complex neurons perform.

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