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Decoding oscillatory representations and mechanisms in memory.

Abstract：

:A fundamental goal in memory research is to understand how information is represented in distributed brain networks and what mechanisms enable its reactivation. It is evident that progress towards this goal will greatly benefit from multivariate pattern classification (MVPC) techniques that can decode representations in brain activity with high temporal resolution. Recently, progress along these lines has been achieved by applying MVPC to neural oscillations recorded with electroencephalography (EEG) and magnetoencephalography (MEG). We highlight two examples of methodological approaches for MVPC of EEG and MEG data that can be used to study memory function. The first example aims at understanding the dynamic neural mechanisms that enable reactivation of memory representations, i.e., memory replay; we discuss how MVPC can help uncover the physiological mechanisms underlying memory replay during working memory maintenance and episodic memory. The second example aims at understanding representational differences between various types of memory, such as perceptual priming and conscious recognition memory. We also highlight the conceptual and methodological differences between these two examples. Finally, we discuss potential future applications for MVPC of EEG/MEG data in studies of memory. We conclude that despite its infancy and existing methodological challenges, MVPC of EEG and MEG data is a powerful tool with which to assess mechanistic models of memory.

journal_name

Neuropsychologia

journal_title

Neuropsychologia

authors

Jafarpour A,Horner AJ,Fuentemilla L,Penny WD,Duzel E

doi

10.1016/j.neuropsychologia.2012.04.002

subject

Has Abstract

pub_date

2013-03-01 00:00:00

pages

772-80

issue

4

eissn

0028-3932

issn

1873-3514

pii

S0028-3932(12)00153-4

journal_volume

51

pub_type

杂志文章

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