From synapse to network: models of information storage and retrieval in neural circuits.
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2021-10
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Abstract
The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information and that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental data and in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations in vivo during delayed response tasks.
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Aljadeff, Johnatan, Maxwell Gillett, Ulises Pereira Obilinovic and Nicolas Brunel (2021). From synapse to network: models of information storage and retrieval in neural circuits. Current opinion in neurobiology, 70. pp. 24–33. 10.1016/j.conb.2021.05.005 Retrieved from https://hdl.handle.net/10161/24167.
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Scholars@Duke
Nicolas Brunel
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.
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