The Sync-Fire/deSync model: Modelling the reactivation of dynamic memories from cortical alpha oscillations

We propose a neural network model to explore how humans can learn and accurately retrieve temporal sequences, such as melodies, movies, or other dynamic content. We identify target memories by their neural oscillatory signatures, as shown in recent human episodic memory paradigms. Our model comprise...

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Bibliographic Details
Published in:Neuropsychologia 2021-07, Vol.158, p.107867-107867, Article 107867
Main Authors: Parish, George, Michelmann, Sebastian, Hanslmayr, Simon, Bowman, Howard
Format: Article
Language:English
Subjects:
Attentional blink
Brain oscillations
Episodic memory model
Temporal sequence model
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Summary:We propose a neural network model to explore how humans can learn and accurately retrieve temporal sequences, such as melodies, movies, or other dynamic content. We identify target memories by their neural oscillatory signatures, as shown in recent human episodic memory paradigms. Our model comprises three plausible components for the binding of temporal content, where each component imposes unique limitations on the encoding and representation of that content. A cortical component actively represents sequences through the disruption of an intrinsically generated alpha rhythm, where a desynchronisation marks information-rich operations as the literature predicts. A binding component converts each event into a discrete index, enabling repetitions through a sparse encoding of events. A timing component – consisting of an oscillatory “ticking clock” made up of hierarchical synfire chains – discretely indexes a moment in time. By encoding the absolute timing between discretised events, we show how one can use cortical desynchronisations to dynamically detect unique temporal signatures as they are reactivated in the brain. We validate this model by simulating a series of events where sequences are uniquely identifiable by analysing phasic information, as several recent EEG/MEG studies have shown. As such, we show how one can encode and retrieve complete episodic memories where the quality of such memories is modulated by the following: alpha gate keepers to content representation; binding limitations that induce a blink in temporal perception; and nested oscillations that provide preferential learning phases in order to temporally sequence events. •A neural network model to encode and reactivate temporally dynamic memory traces.•Explores how alpha oscillations can be decoded to decipher information content.•Identifies how nested oscillations can be used to segregate temporal perception.•Identifies the necessity for a broadly tuned binding pool to discretise input.•Binding processes induce an attentional-blink in perception.
ISSN:0028-3932
1873-3514
DOI:10.1016/j.neuropsychologia.2021.107867