The Sync/deSync model: how a synchronized hippocampus and a desynchronized neocortex code memories

George Parish, Simon Hanslmayr, Howard Bowman

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
156 Downloads (Pure)

Abstract

Neural oscillations are important for memory formation in the brain. The desynchronization of alpha (10 Hz) oscillations in the neocortex has been shown to predict successful memory encoding and retrieval. However, when engaging in learning, it has been found that the hippocampus synchronizes in theta (4 Hz) oscillations, and that learning is dependent on the phase of theta. This inconsistency as to whether synchronization is “good” for memory formation leads to confusion over which oscillations we should expect to see and where during learning paradigm experiments. This paper seeks to respond to this inconsistency by presenting a neural network model of how a well functioning learning system could exhibit both of these phenomena, i.e., desynchronization of alpha and synchronization of theta during successful memory encoding.

We present a spiking neural network (the Sync/deSync model) of the neocortical and hippocampal system. The simulated hippocampus learns through an adapted spike-time dependent plasticity rule, in which weight change is modulated by the phase of an extrinsically generated theta oscillation. Additionally, a global passive weight decay is incorporated, which is also modulated by theta phase. In this way, the Sync/deSync model exhibits theta phase-dependent long-term potentiation and long-term depression. We simulated a learning paradigm experiment and compared the oscillatory dynamics of our model with those observed in single-cell and scalp-EEG studies of the medial temporal lobe. Our Sync/deSync model suggests that both the desynchronization of neocortical alpha and the synchronization of hippocampal theta are necessary for successful memory encoding and retrieval.
Original languageEnglish
Pages (from-to)3428-3440
Number of pages13
JournalThe Journal of Neuroscience
Volume38
Issue number14
Early online date27 Feb 2018
DOIs
Publication statusPublished - 4 Apr 2018

Keywords

  • oscillations
  • computational modeling
  • episodic memory
  • theta
  • alpha
  • EEG

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