Engram size varies with learning and reflects memory content and precision

AbstractHow does the human brain manage multiple bits of information to guide goal-directed behaviour? Successful working memory (WM) functioning has consistently been linked to oscillatory power in the theta frequency band (4-8 Hz) over fronto-medial cortex (fronto-medial theta, FMT). Specifically, FMT is thought to reflect the mechanism of an executive sub-system that coordinates maintenance of memory contents in posterior regions. However, direct evidence for the role of FMT in controlling specific WM content is lacking. Here we collected high-density Electroencephalography (EEG) data whilst participants engaged in load-varying WM tasks and then used multivariate decoding methods to examine WM content during the maintenance period. Higher WM load elicited a focal increase in FMT. Importantly, decoding of WM content was driven by posterior/parietal sites, which in turn showed load-induced functional theta coupling with fronto-medial cortex. Finally, we observed a significant slowing of FMT frequency with increasing WM load, consistent with the hypothesised broadening of a theta ‘duty cycle’ to accommodate additional WM items. Together these findings demonstrate that frontal theta orchestrates posterior maintenance of WM content. Moreover, the observed frequency slowing elucidates the function of FMT oscillations by specifically supporting phase-coding accounts of WM.Significance StatementHow does the brain juggle the maintenance of multiple items in working memory (WM)? Here we show that increased WM demands increase theta power (4-8 Hz) in fronto-medial cortex. Interestingly, using a machine learning approach, we found that the content held in WM could be read out not from frontal, but from posterior areas. These areas were in turn functionally coupled with fronto-medial cortex, consistent with the idea that frontal cortex orchestrates WM representations in posterior regions. Finally, we observed that holding an additional item in WM leads to significant slowing of the frontal theta rhythm, supporting computational models that postulate longer ‘duty cycles’ to accommodate additional WM demands.

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Finding positive meaning in memories of negative events adaptively updates memory

Nature Communications ◽

10.1038/s41467-021-26906-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Megan E. Speer ◽

Sandra Ibrahim ◽

Daniela Schiller ◽

Mauricio R. Delgado

Keyword(s):

Mental Health ◽

Positive Emotion ◽

Memory Representation ◽

Negative Events ◽

Memory Updating ◽

Positive Meaning ◽

Memory Content ◽

Negative Memories

AbstractFinding positive meaning in past negative memories is associated with enhanced mental health. Yet it remains unclear whether it leads to updates in the memory representation itself. Since memory can be labile after retrieval, this leaves the potential for modification whenever its reactivated. Across four experiments, we show that positively reinterpreting negative memories adaptively updates them, leading to the re-emergence of positivity at future retrieval. Focusing on the positive aspects after negative recall leads to enhanced positive emotion and changes in memory content during recollection one week later, remaining even after two months. Consistent with a reactivation-induced reconsolidation account, memory updating occurs only after a reminder and twenty four hours, but not a one hour delay. Multi-session fMRI showed adaptive updates are reflected in greater hippocampal and ventral striatal pattern dissimilarity across retrievals. This research highlights the mechanisms by which updating of maladaptive memories occurs through a positive emotion-focused strategy.

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The phenomenal object of memory and control processes

Behavioral and Brain Sciences ◽

10.1017/s0140525x0004228x ◽

1996 ◽

Vol 19 (2) ◽

pp. 202-203

Author(s):

Giuliana Mazzoni

Keyword(s):

Truth Values ◽

Control Processes ◽

Memory Content ◽

Memory Contents ◽

And Control

AbstractThis commentary deals with criteria for assigning truth values to memory contents. A parallel with perception shows how truth values can be assigned by considering subjects' beliefs about the truth state of the memory content. This topic is also relevant to the study of processes of control over retrieval.

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Examining Autobiographical Memory Content in Patients with Depression and Anxiety Disorders

Cognitive Behaviour Therapy ◽

10.1080/16506073.2010.520730 ◽

2010 ◽

Vol 39 (4) ◽

pp. 302-310 ◽

Cited By ~ 4

Author(s):

Kate S. Witheridge ◽

Christine M. Cabral ◽

Neil A. Rector

Keyword(s):

Anxiety Disorders ◽

Autobiographical Memory ◽

Depression And Anxiety ◽

Memory Content

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Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortex

Neuron ◽

10.1016/j.neuron.2018.07.025 ◽

2018 ◽

Vol 99 (3) ◽

pp. 588-597.e5 ◽

Cited By ~ 13

Author(s):

Simon Nikolas Jacob ◽

Daniel Hähnke ◽

Andreas Nieder

Keyword(s):

Working Memory ◽

Memory Content

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Tracking stimulus representation across a 2-back visual working memory task

Royal Society Open Science ◽

10.1098/rsos.190228 ◽

2020 ◽

Vol 7 (8) ◽

pp. 190228 ◽

Cited By ~ 2

Author(s):

Quan Wan ◽

Ying Cai ◽

Jason Samaha ◽

Bradley R. Postle

Keyword(s):

Working Memory ◽

Visual Working Memory ◽

Memory Task ◽

Memory Item ◽

Neural Representation ◽

Stimulus Representation ◽

Representational Format ◽

The Status ◽

Memory Content ◽

Default Assumption

How does the neural representation of visual working memory content vary with behavioural priority? To address this, we recorded electroencephalography (EEG) while subjects performed a continuous-performance 2-back working memory task with oriented-grating stimuli. We tracked the transition of the neural representation of an item ( n ) from its initial encoding, to the status of ‘unprioritized memory item' (UMI), and back to ‘prioritized memory item', with multivariate inverted encoding modelling. Results showed that the representational format was remapped from its initially encoded format into a distinctive ‘opposite' representational format when it became a UMI and then mapped back into its initial format when subsequently prioritized in anticipation of its comparison with item n + 2. Thus, contrary to the default assumption that the activity representing an item in working memory might simply get weaker when it is deprioritized, it may be that a process of priority-based remapping helps to protect remembered information when it is not in the focus of attention.

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Echo State Property Linked to an Input: Exploring a Fundamental Characteristic of Recurrent Neural Networks

Neural Computation ◽

10.1162/neco_a_00411 ◽

2013 ◽

Vol 25 (3) ◽

pp. 671-696 ◽

Cited By ~ 69

Author(s):

G. Manjunath ◽

H. Jaeger

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Statistical Characteristics ◽

Connection Matrix ◽

Reservoir Computing ◽

Mathematical Methods ◽

Network Response ◽

Network Connection ◽

Memory Content ◽

Definition Of

The echo state property is a key for the design and training of recurrent neural networks within the paradigm of reservoir computing. In intuitive terms, this is a passivity condition: a network having this property, when driven by an input signal, will become entrained by the input and develop an internal response signal. This excited internal dynamics can be seen as a high-dimensional, nonlinear, unique transform of the input with a rich memory content. This view has implications for understanding neural dynamics beyond the field of reservoir computing. Available definitions and theorems concerning the echo state property, however, are of little practical use because they do not relate the network response to temporal or statistical properties of the driving input. Here we present a new definition of the echo state property that directly connects it to such properties. We derive a fundamental 0-1 law: if the input comes from an ergodic source, the network response has the echo state property with probability one or zero, independent of the given network. Furthermore, we give a sufficient condition for the echo state property that connects statistical characteristics of the input to algebraic properties of the network connection matrix. The mathematical methods that we employ are freshly imported from the young field of nonautonomous dynamical systems theory. Since these methods are not yet well known in neural computation research, we introduce them in some detail. As a side story, we hope to demonstrate the eminent usefulness of these methods.

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