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  • Review Article
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The what, where and how of delay activity

Abstract

Working memory is characterized by neural activity that persists during the retention interval of delay tasks. Despite the ubiquity of this delay activity across tasks, species and experimental techniques, our understanding of this phenomenon remains incomplete. Although initially there was a narrow focus on sustained activation in a small number of brain regions, methodological and analytical advances have allowed researchers to uncover previously unobserved forms of delay activity various parts of the brain. In light of these new findings, this Review reconsiders what delay activity is, where in the brain it is found, what roles it serves and how it may be generated.

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Fig. 1: Schematic examples of different types of delay activity.
Fig. 2: Schematic depictions of the properties of delay activity in different brain regions.

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Acknowledgements

The authors thank A. Kiyonaga, E. Lorenc and D. Bliss for their helpful comments on previous versions of this manuscript. This work was supported by US National Institutes of Health Grant MH63901 to M.D.

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Nature Reviews Neuroscience thanks E. K. Miller and the other anonymous reviewers for their contribution to the peer review of this work.

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Both authors researched data for the article and made substantial contributions to the discussion of content. K.K.S. wrote the article and K.K.S. and M.D. reviewed or edited the manuscript before submission.

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Correspondence to Kartik K. Sreenivasan or Mark D’Esposito.

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Glossary

WM delay task

A task that temporally segregates working memory (WM) encoding, maintenance and response by introducing an unfilled memory delay between a memory stimulus and the contingent behavioural response.

Voxels

The volumetric units of functional MRI (fMRI) measurement. A 3D fMRI brain image contains ~100,000 voxels, each of which represents the activity of tens of thousands of neurons.

Population coding

A coding scheme wherein information is encoded in the combined activity of a population of neurons (or electrodes or voxels) as opposed to the activity of individual neurons (or electrodes or voxels).

Haemodynamic response

The temporal pattern of blood oxygen level-dependent signal observed by functional MRI in response to a brief impulse of neural activity. It takes ~20 s to return to baseline.

General linear model

(GLM). A model that describes the output of a system as a linear combination of predictors. GLMs are used to estimate blood oxygen level-dependent responses to features of an experimental task.

Impulse response function

The output of a dynamic system in response to a brief input.

Nonlinear mixed selectivity

A property that allows neurons to respond to combinations of stimulus or task features with nonlinear changes in firing rates.

WM load

The amount of information that is held in working memory (WM). WM load can be manipulated by varying the number or complexity of memory items.

WM capacity

The upper bound on the amount of information that an individual can store at once in working memory (WM).

Encoding models

Models that form a prediction of brain activity for a given set of experimental features (for example, specific memory items during a working memory delay task).

Attractor state

A stable state of the activity of a network of (usually recurrently connected) neurons that persists in the absence of input.

Short-term plasticity

(STP). Synaptic plasticity in response to brief (~1s) stimulation. Hebbian forms (involving presynaptic and postsynaptic changes) and non-Hebbian forms (involving only presynaptic changes) of STP have been proposed to underlie working memory.

Matched filter

A linear filter that can help detect the presence of a known stimulus in a noisy observed signal by correlating the known stimulus with the observed signal.

Time constants

Values that describe the time required for a neuron to return to a baseline state following an input.

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Sreenivasan, K.K., D’Esposito, M. The what, where and how of delay activity. Nat Rev Neurosci 20, 466–481 (2019). https://doi.org/10.1038/s41583-019-0176-7

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