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X-WR-CALNAME:Bordeaux Neurocampus
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X-WR-CALDESC:Events for Bordeaux Neurocampus
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DTSTART;TZID=Europe/Paris:20240208T140000
DTEND;TZID=Europe/Paris:20240208T140000
DTSTAMP:20260410T104226
CREATED:20231003T093748Z
LAST-MODIFIED:20231122T144834Z
UID:162851-1707400800-1707400800@www.bordeaux-neurocampus.fr
SUMMARY:Seminar - Pascale Bomont
DESCRIPTION:Venue: Centre Broca \n\nPascale BOMONT\nERC group leader\, INSERM Research Director\nNeuroMyoGène institute (INMG-PGNM)\nUCBL Lyon1 – CNRS UMR5261 – INSERM U1315\nLyon – France \nInvited by David Perrais (IINS) \nTitle\nDynamics of Neurofilaments in health and disease \nAbstract\nNeurofilaments (NFs) are the Intermediate Filaments (IFs) of the nervous system and the major cytoskeletal component of mature neurons. Previous studies have revealed that this structural network exhibits dynamics in transport and turn-over\, and sustains essential cellular and physiological functions in the nervous system. Accordingly\, alterations of NFs have been shown to drive neurodegeneration in human diseases. Not only are NFs a genetic cause of neuronal death in human\, but their abnormal aggregation is also an early pathological hallmark of most neurodegenerative diseases\, and their removal from axons has shown spectacular benefits in mouse models. With a long-lasting interest in the rare disease giant axonal neuropathy\, whose mutated gene encodes for the universal regulator of IF steady-state (Gigaxonin-E3 ligase)\, our laboratory is exploring the neurobiology of NFs in health and disease. Our research program aims at scrutinizing the dynamics of NFs in a physiological context\, the downstream signaling sustaining their essential functions and how alterations of this system can cause neurodegeneration. Here\, we will first present our research on Gigaxonin to focus on our latest development in generating new tools and methodologies in the zebrafish species\, whose numerous advantages constitute great assets to monitor the live imaging of NFs in vivo and to dissect their molecular signaling pathways. Finally\, we  will present how this knowledge will be used to offer a significant platform for therapeutic intervention in the future\, for the benefit of most neurodegenerative diseases. \nBiosketch\nPascale Bomont received her PhD in Human Genetics in 2002 (IGBMC\, Strasbourg\, France) and identified the mutated genes for several neuropathies.  Focusing on rare diseases\, in particular giant axonal neuropathy (GAN) caused by loss-of-function of the Gigaxonin-E3 ligase\, she conducted a postdoctoral training in Cell Biology (LICR\, San Diego\, USA) to investigate the cytoskeleton alteration in disease.  Moving back to France\, she was recruited at Inserm in 2007 (Assistant Professor) and was awarded by the ATIP-Avenir prize in 2011 to run a multidisciplinary research program on GAN at Montpellier University (Associate Professor in 2012). Her group developed tools and biological systems in patients\, mouse and zebrafish to unravel the key roles of Gigaxonin in controlling cytoskeleton architecture\, autophagy machinery and neuromuscular integrity\, and to generate diagnosis tests and therapeutic approaches for patients. Presently\, she is full Professor at Lyon1 University and runs an ERC program on neurofilament biology. \nSelected publications\nA multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN.\nLescouzères L\, Hassen-Khodja C\, Baudot A\, Bordignon B\, Bomont P. EMBO Mol Med. (2023) May 5:e16267. \nDevelopment of a high-throughput tailored imaging method in zebrafish to understand and treat neuromuscular diseases.\nLescouzères L\, Bordignon B\, Bomont P. Front Mol Neurosci. (2022) Sep 20;15:956582. \nThe dazzling rise of neurofilaments: Physiological functions and roles as biomarkers.\nBomont P. Curr Opin Cell Biol. (2021) Jan13:S0955-0674(20)30146-0. \nE3 Ubiquitin Ligases in Neurological Diseases: Focus on Gigaxonin and Autophagy.\nLescouzères L\, Bomont P. Front Physiol. (2020) Oct 22;11:1022. \nNeurofilaments: neurobiological foundations for biomarker applications.\nGafson AR\, Barthélemy NR*\, Bomont P*\, Carare RO*\, Durham HD*\, Julien JP*\, Kuhle J*\, Leppert D*\, Nixon RA*\, Weller RO*\, Zetterberg H*\, Matthews PM. Brain (2020) 143(7):1975-1998. \nSonic Hedgehog repression underlies gigaxonin mutation-induced motor deficits in giant axonal neuropathy. Arribat Y*\, Mysiak KS*\, Lescouzères L\, Boizot A\, Ruiz M\, Rossel M\, Bomont P.   J Clin Invest. (2019) 129(12):5312-5326. \nGigaxonin E3 ligase governs ATG16L1 turn over to control autophagosome production.\nScrivo A\, Codogno P\, Bomont P. Nat Commun. (2019) 10(1):780. \n
URL:https://www.bordeaux-neurocampus.fr/en/event/seminar-pascale-bomont/
CATEGORIES:For scientists,home-event,Impromptu seminar
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DTSTART;TZID=Europe/Paris:20240208T143000
DTEND;TZID=Europe/Paris:20240208T143000
DTSTAMP:20260410T104226
CREATED:20240129T134405Z
LAST-MODIFIED:20240130T074909Z
UID:167379-1707402600-1707402600@www.bordeaux-neurocampus.fr
SUMMARY:Mini-symposium - Frontiers in Bio-Inspired Learning and Intelligent Systems
DESCRIPTION:Venue: Salle de Conférence du CGFB \n\nOrganized in the frame of Hugo Chateau-Laurent’s thesis defense (February 8th\, 16h30- Broca Center) \nWith (see details below) \n\nRufin VanRullen\nMehdi Khamassi\n\nMehdi Khamassi\nTitle : Model-based and model-free reinforcement learning mechanisms in brains and robot\n\nAbstract : The reinforcement learning (RL) theory constitutes a framework for an artificial agent to learn actions that maximize rewards in the environment. It has been successfully applied to Neuroscience to account for animal neural and behavioral processes in simple laboratory tasks\, such as Pavlovian and instrumental conditioning\, and single-step economic decision-making tasks. It moreover became very popular due to its account for dopamine reward prediction error signals. However\, more complex multi-step tasks\, such as navigation and social interaction tasks\, illustrate their computational limitations.\nIn parallel\, researches in engineering\, in robotics in particular\, have emphasized the complementarity between different learning strategies when facing complex tasks\, and explored solutions to combine these different strategies. One central distinction is between model-based and model-free reinforcement learning strategies: In the former case\, an agent learns a statistical model of the effects of its actions in the environment\, and then use this model to plan sequences of actions towards desired goals. In contrast\, model-free strategies are relevant when the environment statistics are too noisy to learn a good internal model. In this case\, RL agents can rather learn local action values and adapt reactively in each state of the environment.\nIn this presentation\, I will show a series of work where we used a coordination of model-based and model-free reinforcement learning to account for a diversity of behavioral and neural observations in humans\, non-human primates and rodents in different paradigms: Navigation\, instrumental and Pavlovian conditioning. I will moreover present recent robotics results where the same algorithm with the same parameters produces optimal performance in simple navigation and social interaction tasks\, with a drastically reduced computational cost compared to classical methods. Finally\, I will show how the patterns of mental simulation within such internal models can mimic experimentally observed reactivations of the rodent hippocampus in spatial cognition tasks\, and raise new predictions for future experiments.\nRufin VanRullen\nTitle : Brain-inspired multimodal deep learning\n\nAbstract : I will describe recent efforts to design novel deep neural network architectures drawing inspiration from cognitive neuroscience. One example is the design of multimodal (e.g. text+image) architectures following the “Global Workspace” theory of cognitive science. The independent modalities converge onto a common representation space (the global workspace)\, and the shared information is then broadcast back towards the entire system. Such an architecture provides a form of referential meaning or “grounding” to each unimodal system. In addition\, the broadcast mechanism can be trained with unsupervised cycle-consistency objectives\, which makes such systems particularly attractive compared with state-of-the-art models trained on billions of paired data samples (e.g. text+image).\n
URL:https://www.bordeaux-neurocampus.fr/en/event/mini-symposium-modelisation-cognitive-et-computationnelle/
CATEGORIES:For scientists,home-event,Symposium
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