Recently, some of the neuronal correlates of high level cognitive functions, such as decision making, working memory, visual perception and attention, have been uncovered. Neurophysiologists, powered with new recording techniques, are obtaining crucial information about the neuronal mechanisms that underlay the perception of the objects, confidence in our decisions or our perception of time. With these neuronal recording techniques, we are for the first time in a position to unravel some of the complex mechanisms that underlay basic psychological phenomena. However, the interpretation of the data is often difficult due to their richness and complexity, and a theoretical framework that would allow interpreting these neuronal data coherently is still lacking.
To describe and understand the complex, high dimensional neuronal data arising from state of the art recording techniques and being able to link them to behavior, we will develop a new mathematical framework that is algorithmically efficient and at the same time incorporates our knowledge about neuronal electrophysiology. In this multidisciplinary project we will combine computational neuroscience with the analysis of neuronal data to (a) understand how neuronal networks can generate non-trivial behaviours, (b) predict neuronal connectivity from the observed activity patterns, and (c) correlate activity patterns to psychological phenomena.
We will analyze and model data from multielectrode recordings in monkey primary visual cortex using state of the art and our own developments of analytical, computational and simulation techniques in order to address the fundamental question of how neurons interact to lead to complex activity patterns. This project will lead to a novel technique for ‘drawing neuronal circuits without seeing them’, and migth have important consequences for understanding mental disorders originated in defects of neuronal circuits.
Fields of science
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