Descripción del proyecto
La modelización de nuestro sistema visual
A lo largo de los años, se han generado muchos modelos para intentar reproducir los procesos que tienen lugar durante la percepción visual. Sin embargo, hay muchos factores que influyen en nuestra forma de ver las cosas, lo que hace que el proceso real sea muy complejo. El objetivo del proyecto DEEPRETINA, financiado por el Consejo Europeo de Investigación, es desarrollar modelos realistas de redes profundas que puedan predecir las respuestas neuronales a diversos estímulos. Los modelos reconstruirán la red retiniana de células ganglionares que transfieren la información de la retina a los centros de procesamiento visual del cerebro. Se espera que la novedad del método DEEPRETINA ofrezca una simulación realista del sistema visual.
Objetivo
A major goal of sensory neuroscience is to understand how sensory neurons process natural scenes. Models built from the responses of sensory neurons to simple stimuli do not generalize to predict how complex, natural scene are processed. Even as early as in the retina, this issue is not solved. Deep network models have been proposed to predict the responses of visual neurons to natural stimuli. However, they are still far from being a realistic model of the visual system. First, the sensitivity to perturbations of the stimulus can thus be very different for a deep network model and for our visual system. Second, it is not clear how the model components can be related to actual mechanisms in the brain. Our purpose is to understand how the retina processes natural scenes. We will follow an interdisciplinary approach where we will build realistic deep network models of retinal processing and test them in experiments. We will develop deep network models that can predict ganglion cell responses to natural stimuli, and map the components of these models to specific cell types in the retinal network. Our project is original because it will use two novel methods, that will be key to achieve our goal. The first one is a novel approach to characterize retinal function, where we will probe the selectivity of the retina to perturbations of natural stimuli. The second one is a novel tool based on 2-photon holographic stimulation to decompose the retinal circuit. They are tailored to address the specific issues of deep networks. Each ganglion cell has a receptive field center, the region of visual space whose stimulation evokes the strongest responses. Our project is divided in three parts. We will first understand how natural images are integrated inside the receptive field center. We will then ask how stimulation outside the receptive field center affects ganglion cell processing of natural images. Finally, we will focus on motion processing during natural scene stimulation.
Ámbito científico
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-AG - HORIZON Action Grant Budget-BasedInstitución de acogida
75654 Paris
Francia