Overview
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.
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.
Current Appointments & Affiliations
Adjunct Professor of Neurobiology
·
2024 - Present
Neurobiology,
Basic Science Departments
Faculty Network Member of the Duke Institute for Brain Sciences
·
2018 - Present
Duke Institute for Brain Sciences,
University Institutes and Centers
Member of the Center for Cognitive Neuroscience
·
2018 - Present
Center for Cognitive Neuroscience,
Duke Institute for Brain Sciences
Education, Training & Certifications
Pierre and Marie Curie University (France) ·
1993
Ph.D.