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
Main Authors: Bowman, H. (Author), Hanslmayr, S. (Author), Michelmann, S. (Author), Parish, G. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2021
Subjects:
Online Access:View Fulltext in Publisher
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001 10.1016-j.neuropsychologia.2021.107867
008 220427s2021 CNT 000 0 und d
020 |a 00283932 (ISSN) 
245 1 0 |a The Sync-Fire/deSync model: Modelling the reactivation of dynamic memories from cortical alpha oscillations 
260 0 |b Elsevier Ltd  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.neuropsychologia.2021.107867 
520 3 |a 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. © 2021 The Authors 
650 0 4 |a alpha rhythm 
650 0 4 |a alpha rhythm 
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650 0 4 |a attentional blink 
650 0 4 |a Attentional blink 
650 0 4 |a brain 
650 0 4 |a Brain 
650 0 4 |a Brain oscillations 
650 0 4 |a electroencephalogram 
650 0 4 |a episodic memory 
650 0 4 |a Episodic memory model 
650 0 4 |a human 
650 0 4 |a Humans 
650 0 4 |a learning 
650 0 4 |a Learning 
650 0 4 |a Memory, Episodic 
650 0 4 |a oscillation 
650 0 4 |a Temporal sequence model 
650 0 4 |a validation process 
700 1 |a Bowman, H.  |e author 
700 1 |a Hanslmayr, S.  |e author 
700 1 |a Michelmann, S.  |e author 
700 1 |a Parish, G.  |e author 
773 |t Neuropsychologia