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个人简介
We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods from
statistical physics, we have shown recently that the synaptic
connectivity of a network that maximizes storage capacity reproduces
two key experimentally observed features: low connection probability
and strong overrepresentation of bidirectionnally connected pairs of
neurons. We have also inferred `synaptic plasticity rules' (a
mathematical description of how synaptic strength depends on the
activity of pre and post-synaptic neurons) from data, and shown that
networks endowed with a plasticity rule inferred from data have a
storage capacity that is close to the optimal bound.
研究兴趣
论文共 148 篇作者统计合作学者相似作者
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Krithika Mohan, Ulises Pereira Obilinovic, Stanislav Srednyak,Yali Amit,Nicolas Brunel,David J Freedman
biorxiv(2024)
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biorxiv(2023)
WORLD SCIENTIFIC eBookspp.499-521, (2023)
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bioRxiv (Cold Spring Harbor Laboratory) (2023)
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Yi Li,Xu An,Yongjun Qian, X Hermione Xu,Shengli Zhao,Hemanth Mohan, Ludovica Bachschmid-Romano,Nicolas Brunel,Ian Q Whishaw,Z Josh Huang
bioRxiv : the preprint server for biology (2023)
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