Contributors: Décision et processus Bayesiens - Decision and Bayesian Computation; Institut Pasteur Paris (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité); Approches expérimentales et numériques pour explorer le cerveau des insectes (EPIMETHEE); Institut Pasteur Paris (IP)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria); Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB; Institut Pasteur Paris (IP)-Université Paris Cité (UPCité); University of Cambridge Cambridge, UK (CAM); Laboratory of Molecular Biology Cambridge; Medical Research Council; Howard Hughes Medical Institute (HHMI); Algorithmes pour les séquences biologiques - Sequence Bioinformatics; Institut des Neurosciences Paris-Saclay (NeuroPSI); Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS); This study was funded by the INCEPTION project (PIA/ANR-16-CONV-0005), the “Investissements d’avenir” program managed by Agence Nationale de la Recherche, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) to J.B.M, C.L.V, C.B & A.B, and Agence Nationale de la Recherche ANR-20-CE45-0021 to C.L.V. and A.B. This work was supported by ANR PIA funding: ANR-20-IDEES-0002 (T.J), Agence Nationale de la Recherche (ANR-17-CE37-0019-01) (T.J), ANR-NEUROMOD (ANR-22-CE37-0027) (T.J). This project has also received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 798050 (T.J & J.B.M). L.M was supported by the Amgen Scholars program.; ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016); ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019); ANR-20-CE45-0021,SiNCoBe,Base structurelle du calcul neuronal et du comportement(2020); ANR-20-IDES-0002,HISTOIRE,Ressources Humaine, Internale, Développement Socio-économique, Territoire, Ouverture, NumérIque pour le REnouvellement(2020); ANR-17-CE37-0019,DECISIONSEQ,Base neuronale de la prise de decision et de sequences compartementales(2017); ANR-22-CE37-0027,NeuroMOD,Modulation des circuit neuronaux sous-jacents aux décisions sensorimotrices par l'état interne(2022); European Project: 798050,H2020-MSCA-IF-2017,H2020-MSCA-IF-2017,DECISIONSEQ(2018)
نبذة مختصرة : Posted May 05, 2024 on bioRxiv. ; International audience ; The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours. These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines. Author Summary There is a significant gap in understanding between the architecture of neural circuits and the mechanisms of action selection and behaviour generation. Drosophila larvae ...
Processing Request
No Comments.