Pantheon SEMPARIS Le serveur des séminaires parisiens Paris

Statut Confirmé
Date Vendredi 11 Mars 2022
Heure 13:00
Institut LPENS
Salle Salle Favard IBENS
Nom de l'orateur Vestergaard
Prenom de l'orateur Christian
Addresse email de l'orateur
Institution de l'orateur Institute Pasteur
Titre Identifying neural microcircuits in the brains of small animals
Résumé Behavior and decision-making are determined by physical processes taking place in the complex environment of the brain. Experimental techniques have reached the point where it is now possible to map the complete wiring diagram (the physical connectome of synaptic connections between neurons) of the brain of simple model organisms at the level of single synapses, and to manipulate individual neuron activity in freely moving animals and observe the resulting behavior. Together this lets us investigate how the structure of the connectome constrains an organism’s capability to process information and generate behavior. I will discuss how we can combine knowledge of an animal’s connectome with large-scale behavioral experiments to link neural circuits to decision making and specific behavioral sequences. I will mainly focus on how to extract the statistical regularities (i.e., “motifs”) of a connectome of an animal. Relying on a restricted set of statistically regular circuit motifs, optimized for specific functions, may provide an animal with biologically advantageous inductive biases for efficient learning and help encode innate behaviors. Information theory furthermore tells us that the presence of statistically regularities would make the connectome compressible, and circuit motifs would thus provide a means to encode the neural wiring information in the limited storage space of the genome. Identifying motifs in a connectome is a challenging inverse problem since we have access to only a single experimental realization (i.e., a single graph). To circumvent problems with classic null-model-based analysis linked to multiple testing and the ill- posed problem of defining a proper null model against which statistical significance is defined, we have developed methods combining hierarchies of microcanonical random graph null models and graph compression techniques. We applied our methods to uncover circuit motifs in different brain regions of adult and larval Drosophila as well as C. elegans in different developmental stages. Our preliminary results show more compressible yet more complex brain structure in larger brains. By comparing typical circuit structures in different brains regions and animals, we may furthermore formulate hypotheses linking circuit structure to function that can be tested in behavioral experiments. I will finally discuss how we can build generative models of neural connectomes.
Numéro de preprint arXiv
Commentaires Zoom link: Meeting ID: 876 1208 9313 Passcode: 274373
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