Neural mechanisms of attending to items in working memory

Manohar, Sanjay; Zokaei, Nahid; Fallon, Sean James; Vogels, Tim P.

doi:10.1016/j.neubiorev.2019.03.017

Cited by 129 publications

(165 citation statements)

References 127 publications

(182 reference statements)

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“…The tempotron classifies a spatiotemporal pattern by either producing a spike or not [38]. More recent studies on sequential working memory propose similar model architectures that enable the use of Hebbian plasticity [39]. For example, spatiotemporal input patterns can be encoded in a set of feedforward synapses using STDP-type rules [40,41].…”

Section: Discussionmentioning

confidence: 99%

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

2020

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Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.

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Section: Discussionmentioning

confidence: 99%

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

2020

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“…At the level of control, priority-based remapping has been modeled as a consequence of competition between prefrontal pointers that activate their corresponding perceptual representations with different levels of priority [18,37]. Whether the effects that we observed in parietal cortex may also reflect the operation of a source of the priority-based control of information held in working memory, as implemented via the priority-sensitive representation of stimulus context, is an important question for future research.…”

Section: Priority-based Remapping In Visual Working Memorymentioning

confidence: 92%

Different states of priority recruit different neural representations in visual working memory

2020

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We used functional magnetic resonance imaging (fMRI) to investigate the neural codes for representing stimulus information held in different states of priority in working memory. Human participants (male and female) performed delayed recall for 2 oriented gratings that could appear in any of several locations. Priority status was manipulated by a retrocue, such that one became the prioritized memory item (PMI) and another the unprioritized memory item (UMI). Using inverted encoding models (IEMs), we found that, in early visual cortex, the orientation of the UMI was represented in a neural representation that was rotated relative to the PMI. In intraparietal sulcus (IPS), we observed the analogous effect for the representation of the location of the UMI. Taken together, these results provide evidence for a common remapping mechanism that may be responsible for representing stimulus identity and stimulus context with different levels of priority in working memory.

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“…Figure 4 illustrates how we believe the existing framework should be extended. Specifically, we propose that multiple templates may hold each other in a mutually competitive relationship within VWM, most likely through laterally suppressive connections (50). Figure 4A depicts the situation when just one of the target features is then encountered in the sensory input.…”

Section: Running Title: Competition In Multiple Target Selectionmentioning

confidence: 99%

Humans can efficiently look for but not select multiple visual objects

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Fahrenfort

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et al. 2019

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Human observers have the remarkable ability to efficiently prioritize task-relevant over taskirrelevant visual information. Yet, a fundamental question remains whether this ability is limited to a single task relevant item, or whether multiple items can be prioritized simultaneously. The answer to this question depends on 1) whether observers can concurrently prepare and maintain multiple top-down templates for more than one target object, and 2) whether those templates can then, in parallel, bias selection towards more than one target in the visual input. Here we disentangle these two processes for the first time.We measured electroencephalographic (EEG) responses while observers searched for two color-defined targets among distractors. Crucially, we not only varied the number of target colors that observers anticipated (thus determining the number of target templates), but also the number of colors used to distinguish the two target objects present in the search display (thus determining the number of templates required to engage in actual selection).Multivariate classification of the EEG pattern allowed us to track the attentional enhancement of each target separately across time. Both behavioral and electrophysiological results revealed only a small cost associated with preparing two versus one color template. In contrast, substantial costs arose when two templates had to be engaged in the actual selection of search targets. Furthermore, the results indicate that this cost is based on limitations of parallel processing, rather than a serial bottleneck. These findings bridge currently diverging theoretical perspectives on capacity limitations of feature-based attention.

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Neural mechanisms of attending to items in working memory

Abstract: Highlights Evidence suggests both sustained activity and synaptic plasticity support working memory. Rapid Hebbian plasticity can support flexible attractor states analogous to a focus of attention. Plastic attractors can account for dynamic neural shifts in memory representations.

Cited by 129 publications

References 127 publications

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Different states of priority recruit different neural representations in visual working memory

Humans can efficiently look for but not select multiple visual objects

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