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Bernstein Group for Computational Neuroscience (BGCN)

"The grounding of higher brain function in dynamic neural fields"

Neural activity will be observed through real-time optical imaging techniques and parallel electrical recording. These data lend themselves to the identification of the underlying network dynamics, which will be achieved using machine learning techniques. Constructing distributions of population activation over functionally relevant feature dimensions, neuronal interactions will be estimated in terms of the width and strength of interaction kernels. It will be investigated theoretically and experimentally how the representation of visual space supports the integration of multiple visual features. When perceptual decisions bring visual objects into the foreground and select associated feature values, the interactive neural dynamics go through instabilities that we will characterize theoretically and strive to detect experimentally. The processes of learning and adaptation will be studied in theoretical modelling with the longer-term goal of setting up an approach to their experimental identification.