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Multimodal integration and population dynamics in the Deep Cerebellar Nuclei


The cerebellum is a key structure of the central nervous system that contains more than half the neurons of the brain. It is highly conserved across vertebrates and crucial for coordinated movement, motor learning and cognition. Cerebellar dysfunction causes a wide range of motor (ataxia, dystonia) or non-motor (autism, schizophrenia) disorders. It is made of two structures: the cerebellar cortex and the Deep Cerebellar Nuclei (DCN). While the former has been thoroughly studied, the DCN - the actual output of the structure - are much less understood. Technological limitations and experimental difficulties have limited the study of DCN processing rules both at the cellular and at the population level. A multidisciplinary approach combining recent discoveries on DCN structure, modern cell type labelling strategies and optogenetics, and novel optical tools based on work performed during my first postdoctoral project (acousto-optic 2-photon imaging coupled with GRIN lenses) will address the following fundamental open questions that limit our understanding of sensorimotor systems: 1) How are inputs carrying sensorimotor information from different parts of the brain (mossy fibres (MFs) and climbing fibres (CFs)) integrated by individual DCN neurons? 2) How are CFs and MFs carrying different sensory modalities processed by DCN neurons at the neuronal and dendritic levels? And how does it relate to distinct DCN neuron subpopulations? 3) What is the dynamic of these subpopulations during a behavioural task in vivo? The outcome of the DeepPop project will provide new knowledge of the computations performed in the cerebellum, novel optical solutions to study deep brain structures and data that will be used by the wider community (e.g. modellers, theoretician, clinicians) and increase our general understanding of sensorimotor systems.

Call for proposal

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Funding Scheme

MSCA-IF-EF-ST - Standard EF


Rue Michel Ange 3
75794 Paris
Activity type
EU contribution
€ 196 707,84