How can we understand how neural circuits process incoming sensory signals, such that we are able to recognize a face in a crowd, or understand human speech? A common, ‘bottom-up’, approach is to try and understand how the properties of ion channels account for single neuron dynamics, how the properties of single neurons account for neural circuit dynamics, and so on. While this has its merits, in practice it can be extremely difficult to go all the way from models of single neurons, or circuits, to predict behavior. Further, even if one can accurately simulate neural circuit dynamics, there is no guarantee of being able to understand their behavior, or deduce how the observed dynamics allow the brain to perform computations.
In my work, I take the opposite ‘top-down’ approach. That is, I start by considering general problems faced by low-level sensory systems (such as detecting edges in an image, or local motion), and then use tools from engineering and computer science to solve these problems, within the constraints faced by the brain. In this way, I hope to gain insights about the principles underlying neural computation, and make predictions about neural activity that can be tested experimentally.
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2017-
The Vision Institute, at the Sorbonne Université, Paris.
Since 2018: INSERM researcher (CRCN)
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2015-2017
Post-doc at IST Austria with Gasper Tkacik
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2012-2015
Post-doc at Ecole Normale Supérieure with Sophie Deneve & Boris Gutkin
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2007-2015
Master in Theoretical Neuroscience at the University of Edinburgh,
followed by a PhD with Peggy Seriés
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2003-2007
Master in Physics at the University of Oxford