Summary
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.
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| Web resources: | https://cordis.europa.eu/project/id/101045253 |
| Start date: | 01-10-2022 |
| End date: | 30-09-2027 |
| Total budget - Public funding: | 1 998 280,00 Euro - 1 998 280,00 Euro |
Cordis data
Original description
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.Status
SIGNEDCall topic
ERC-2021-COGUpdate Date
09-02-2023
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