Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory

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2019-03-01

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Abstract

<jats:p>Episodic memory retrieval relies on the recovery of neural representations of waking experience. This process is thought to involve a communication dynamic between the medial temporal lobe memory system and the neocortex. How this occurs is largely unknown, however, especially as it pertains to awake human memory retrieval. Using intracranial electroencephalographic recordings, we found that ripple oscillations were dynamically coupled between the human medial temporal lobe (MTL) and temporal association cortex. Coupled ripples were more pronounced during successful verbal memory retrieval and recover the cortical neural representations of remembered items. Together, these data provide direct evidence that coupled ripples between the MTL and association cortex may underlie successful memory retrieval in the human brain.</jats:p>

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10.1126/science.aau8956

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Vaz, Alex P, Sara K Inati, Nicolas Brunel and Kareem A Zaghloul (2019). Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science, 363(6430). pp. 975–978. 10.1126/science.aau8956 Retrieved from https://hdl.handle.net/10161/18101.

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Vaz

Alex Vaz

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Brunel

Nicolas Brunel

Duke School of Medicine Distinguished Professor in Neuroscience

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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