We are organizing a one-day workshop for computational neuroscientists in Marseille. The workshop will be held on Monday, June 3rd, from 9:00 am to 5:00 pm at the Amphithéâtre d’Odontologie, Faculté des Sciences Médicales et Paramédicales, Timone campus. This event aims to bring together computational neuroscientists from the Marseille area to foster collaboration and exchange ideas.
When? 3rd of June 2024
Organizers: Fanny Cazettes (INT), Spase Petkoski (INS), Etienne Thoret (INT), Lorenzo Fontolan (INMED)
The organizing committee thanks NeuroMarseille and the three partners institutes (INS, INMED, INT) for their support.
9:00 : Welcome
9:15 : Introduction by Frédéric Chavane (INT, Marseille)
9:30 : Lorenzo Fontolan (CENTURI, INMED): Neural circuits for temporal flexibility
10:00 : Mirindra Ratsifandrihamanana (INMED): Linking early hippocampal dynamics and animal behavior using information theory in physiological and pathological conditions
10:20 : coffee break @ INT-R+4
10:45 : Christophe Bernard (INS): Complexities in interpretations of opto-chemogenetics effects and of dimensionality reduction
11:15 : Borana Dollomaja (INS): Diagnostic approaches for drug-resistant epilepsy: whole brain models and brain stimulation
11:35 : Andrea Brovelli (INT): Brain interactions and goal-directed learning
12:05 : Adrien Fois (INT): Unsupervised Learning of Spiking Motifs
12:30 : Lunch break @ INT-R+4
14:00 - NeuroMarseille - Anne Kavounoudias (CRPN) & Guillaume Masson (INT)
14:15 - eBrains - Viktor Jirsa (INS) - European infrastructure for digital neuroscience
14:30 - CENTURI - Rosa Cossart (INMED)
14:45 - ILCB - Johannes Ziegler (CRPN)
15:00 - Laënnec - Guillaume Auzias (INT)
15:15 - CONECT - Anna Montagnini (INT)
15:30 : Coffee break @ INT-R+4
Abstract: Neural computations are currently conceptualised using two separate approaches: sorting neurons into functional sub-populations or examining distributed collective dynamics. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from recurrent networks trained on neuroscience tasks, we show that the collective dynamics and sub-population structure play fundamentally complementary roles. Although various tasks can be implemented in networks with fully random population structure, we found that flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a sub-population organisation enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics.