A fundamental goal of system neuroscience is to understand how the brain processes sensory information. In this project we go beyond the classical open-loop approach and study the visual system in a closed-loop manner by using advanced optimization algori

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A fundamental goal of system neuroscience is to understand how the brain processes sensory information. Yet, after decades of research we still do not have models that can completely capture the response selectivity of neurons in primary visual cortex.Current open-loop methods that make use linear-system theory fall short in characterizing non-linear neurons, the prevailing neuron type in the primary visual cortex. Furthermore, it is controversial discussed how stimuli are encoded in the activity of single neurons and/or populations of neurons. Characterization of non-linear neurons and a better understanding of the relationship between single neuron and population activity would greatly advance our knowledge about the function of the primary visual cortex and cortex in general.Going beyond the classical open-loop approach by modifying the stimulus based on the neuronal responses in a closed-loop way is the key to these questions. Advances in the field of evolutionary computation have produced powerful optimization algorithms, e.g. algorithms that make use of swarm intelligence. These algorithms can cope with high dimensional parameter spaces and non-linear and noisy objective functions that show multimodal parameter distributions and adaptation. Thus they are very suited to optimize visual stimuli based on neuronal responses.In this project we will study single neurons and populations of neurons in a closed-loop fashion. We will use the optimization algorithms to construct optimal stimulus ensembles based on feedback of single neurons. This will allow us to investigate single neuron properties and their relationship to the embedding population and advance our knowledge about the principles of sensory processing.