scholarly journals Multiple forms of working memory emerge from synapse-astrocyte interactions

2021 ◽  
Author(s):  
Maurizio De Pitta ◽  
Nicolas Brunel

Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons, or by time-varying activity across a neural population, or by activity-silent mechanisms carried out by hidden internal states of the neural population. While WM is traditionally regarded to originate exclusively from neuronal interactions, cortical networks also include astrocytes that can modulate neural activity. We propose that different mechanisms of WM can be brought forth by astrocyte-mediated modulations of synaptic transmitter release. In this account, the emergence of different mechanisms depends on the network's spontaneous activity and the geometry of the connections between synapses and astrocytes.

Cell Reports ◽  
2020 ◽  
Vol 32 (6) ◽  
pp. 108006 ◽  
Author(s):  
Xiyuan Jiang ◽  
Hemant Saggar ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy ◽  
Jonathan C. Kao

2021 ◽  
Author(s):  
Clayton E Curtis ◽  
Thomas C Sprague

Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major arcs of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. A comprehensive theory of WM will require a unification of these two arcs of research.


2020 ◽  
Author(s):  
Mark G. Stokes ◽  
Paul S. Muhle-Karbe ◽  
Nicholas E. Myers

Working memory (WM) is important for guiding behaviour, but not always immediately. Here we define a WM item that is currently relevant for guiding behaviour as the functionally ‘active’ item; whereas items maintained in WM, but not immediately relevant to behaviour, are functionally ‘latent’. Traditional neurophysiological theories of WM proposed that content is maintained via persistent neural activity (e.g., stable attractors); however, more recent theories have highlighted the potential role for ‘activity-silent’ mechanisms (e.g., short-term synaptic plasticity). Given these somewhat parallel dichotomies, it is tempting to associate functionally active and latent cognitive states of WM with persistent- activity and activity-silent neural mechanisms, respectively. In this article we caution against a one-to-one correspondence between functional and activity states. We argue that the principal theoretical requirement for active and latent WM is that the corresponding neural states play qualitatively different functional roles. We consider a number of candidate solutions, and conclude that the neurophysiological mechanisms for functionally active and latent WM items are theoretically independent of the distinction between persistent activity vs activity-silent WM.


2021 ◽  
Vol 15 ◽  
Author(s):  
Clayton E. Curtis ◽  
Thomas C. Sprague

Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major “arcs” of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. Our goal in doing so is to identify barriers to developing a comprehensive theory of WM that will require a unification of these two “arcs” of research.


2018 ◽  
Author(s):  
David W. Sutterer ◽  
Joshua J. Foster ◽  
Kirsten C.S. Adam ◽  
Edward K. Vogel ◽  
Edward Awh

AbstractA longstanding view holds that information is maintained in working memory (WM) via persistent neural activity that encodes the content of WM. Recent work, however, has challenged the view that all items stored in WM are actively maintained. Instead, “activity-silent” models propose that items can be maintained in WM without the need for persistent neural activity, raising the possibility that only a subset of items – perhaps just a single item – may be actively represented at a given time. While past studies have successfully decoded multiple items stored in WM, these studies cannot rule out an active switching account in which only a single item is actively represented at a time. Here, we directly tested whether multiple representations can be held concurrently in an active state. We tracked spatial representations in WM using alpha-band (8–12 Hz) activity, which encodes spatial positions held in WM. Human observers (male and female) remembered one or two positions over a short delay while we recorded EEG. Using a spatial encoding model, we reconstructed stimulus-specific working memory representations (channel tuning functions, CTFs) from the scalp distribution of alphaband power. Consistent with past work, we found the selectivity of spatial CTFs was lower when two items were stored than when one item was stored. Critically, data-driven simulations revealed that the selectivity of spatial representations in the two-item condition could not be explained by models restricting storage to a single item at a time. Thus, our findings provide robust evidence for the concurrent storage of multiple items in visual working memory.Author SummaryWorking memory (WM) is a mental workspace where we temporarily hold information “online” in pursuit of our current goals. However, recent activity-silent models of WM have challenged the view that all items are held in an “online” state, instead proposing that only a subset of representations in WM – perhaps just one item – are represented by persistent activity at a time. To directly test a single-item model of persistent activity, we used a spatial encoding model to read out the strength of two representations from alpha-band (8–12 Hz) power in the human EEG signal. We provide direct evidence that both locations were maintained concurrently, ruling out the possibility that declines in stimulus-specific activity are due to storing one of two items in an activity-silent state.


2017 ◽  
Author(s):  
SE Cavanagh ◽  
JP Towers ◽  
JD Wallis ◽  
LT Hunt ◽  
SW Kennerley

AbstractCompeting accounts propose that working memory (WM) is subserved either by persistent activity in single neurons or by dynamic (time-varying) activity across a neural population. Here we compare these hypotheses across four regions of prefrontal cortex (PFC) in a spatial WM task, where an intervening distractor indicated the reward available for a correct saccade. WM representations were strongest in ventrolateral PFC (VLPFC) neurons with higher intrinsic temporal stability (time-constant). At the population-level, although a stable mnemonic state was reached during the delay, this tuning geometry was reversed relative to cue-period selectivity, and was disrupted by the distractor. Single-neuron analysis revealed many neurons switched to coding reward, rather than maintaining task-relevant spatial selectivity until saccade. These results imply WM is fulfilled by dynamic, population-level activity within high time-constant neurons. Rather than persistent activity supporting stable mnemonic representations that bridge distraction, PFC neurons may stabilise a dynamic population-level process that supports WM.


Cell Reports ◽  
2020 ◽  
Vol 32 (10) ◽  
pp. 108148
Author(s):  
Xiyuan Jiang ◽  
Hemant Saggar ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy ◽  
Jonathan C. Kao

2020 ◽  
Author(s):  
Jay A. Hennig ◽  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Lindsay A. Bahureksa ◽  
Patrick T. Sadtler ◽  
...  

AbstractInternal states such as arousal, attention, and motivation are known to modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain must modify the neural activity it produces to improve behavioral performance. How do internal states affect the evolution of this learning process? Using a brain-computer interface (BCI) learning paradigm in non-human primates, we identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These fluctuations drove population activity along dimensions we term neural engagement axes. Neural engagement increased abruptly at the start of learning, and then gradually retreated. In a BCI, the causal relationship between neural activity and behavior is known. This allowed us to understand how these changes impacted behavioral performance for different task goals. We found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others.


2021 ◽  
pp. 1-41
Author(s):  
Russell J. Jaffe ◽  
Christos Constantinidis

2018 ◽  
Author(s):  
Chethan Pandarinath ◽  
K. Cora Ames ◽  
Abigail A Russo ◽  
Ali Farshchian ◽  
Lee E Miller ◽  
...  

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.


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