Reevaluating the Role of Persistent Neural Activity in Short-Term Memory

Event-specific and persistent representations for contextual states in orbitofrontal neurons

Flexible and context-dependent behaviors require animals, including humans, to identify their current contextual state for proper rules to apply, especially when information that defines these states is partially observable. Depending on behavioral needs, contextual states usually persist for prolonged periods and across other events, including sensory stimuli, actions, and rewards, highlighting prominent challenges of holding a reliable state representation. The orbitofrontal cortex (OFC) is crucial in behaviors requiring the identification of the current context (e.g., reversal learning); however, how single units in the OFC accomplish this function has not been assessed. Do they maintain such information persistently, in separate populations from those responding phasically to events within a task, or is contextual information dynamic and embedded in these phasic responses? Here, we investigated this question by recording single units from OFC in rats performing a task that required them to identify the current contextual state related to estimated proximity to future reward with distracting olfactory cues. We found that while some OFC neurons encode contextual states, most change their selectivity upon the transition of task events. Nevertheless, despite dynamic activities in single neurons, the neural populations maintain persistent representations regarding current contextual states within particular neural subspaces.

Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex

The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.

Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks

Humans' ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.

"Leader-Follower" Dynamic Perturbation Manipulates Multi-Item Working Memory in Humans

Manipulating working memory (WM) is a central yet challenging notion. Previous studies suggest that WM items with varied memory strengths reactivate at different latencies, supporting a time-based mechanism. Motivated by this view, here we developed a purely bottom-up "Leader-Follower" behavioral approach to manipulate WM in humans. Specifically, task-irrelevant flickering color disks that are bound to each of the memorized items are presented during the delay period, and the ongoing luminance sequences of the color disks follow a Leader-Follower relationship, that is, a hundreds of milliseconds temporal lag. We show that this dynamic behavioral approach leads to better memory performance for the item associated with the temporally advanced luminance sequence (Leader) than the item with the temporally lagged luminance sequence (Follower), yet with limited effectiveness. Together, our findings constitute evidence for the essential role of temporal dynamics in WM operation and offer a promising, noninvasive WM manipulation approach.

Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance

Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction.

Neural dynamics underlying successful auditory short-term memory performance

Listeners often operate in complex acoustic environments, consisting of many concurrent sounds. Accurately encoding and maintaining such auditory objects in short-term memory is crucial for communication and scene analysis. Yet, the neural underpinnings of successful auditory short-term memory (ASTM) performance are currently not well understood. To elucidate this issue, we presented a novel, challenging auditory delayed match-to-sample task while recording MEG. Human participants listened to 'scenes' comprising three concurrent tone pip streams. The task was to indicate, after a delay, whether a probe stream was present in the just-heard scene. We present three key findings: First, behavioural performance revealed faster responses in correct versus incorrect trials as well as in 'probe present' versus 'probe absent' trials, consistent with ASTM search. Second, successful compared with unsuccessful ASTM performance was associated with a significant enhancement of event-related fields and oscillatory activity in the theta, alpha and beta frequency ranges. This extends previous findings of an overall increase of persistent activity during short-term memory performance. Third, using distributed source modelling, we found these effects to be confined mostly to sensory areas during encoding, presumably related to ASTM contents per se. Parietal and frontal sources then became relevant during the maintenance stage, indicating that effective STM operation also relies on ongoing inhibitory processes suppressing task-irrelevant information. In summary, our results deliver a detailed account of the neural patterns that differentiate successful from unsuccessful ASTM performance in the context of a complex, multi-object auditory scene.

Spatial Configuration Effects on the Dissociation between Active and Latent States in Visual Working Memory

Visual working memory includes both active, recallable items and latent items not directly available for recall. During the online manipulation of active working memory, latent items gain robust retention. According to the dissociation account, active and passive memories exist in independent states, both of which take up their own cognitive resources. However, it is unclear whether dissociation is a universal phenomenon during memory maintenance. Given that memory information is retained as a binding of identity and location, the spatial proximity of memory items might shield the dissociation property. To test this, we adopted a retro-cue memory task where the cued and uncued items were separated in the active and latent states. In Experiment 1, the memory items were presented at a relatively large spatial distance. The results supported the dissociation account for well-separated items. However, Experiment 2 rejected the dissociation for closer-in items, possibly because items in visual working memory were spatially labeled. These findings suggest that while memory maintenance generally conforms to the dissociation account, the spatial configuration of memory items can affect the dissociation property between the active and latent neural states in visual working memory.

A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions

Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.

Causal evidence for the role of the sensory visual cortex in visual short-term memory maintenance

The role of the sensory visual cortex during visual short-term memory (VSTM) remains controversial. This controversy is possibly due to methodological issues in previous attempts to investigate the effects of transcranial magnetic stimulation (TMS) on VSTM. The aim of this study was to use TMS, while covering previous methodological deficits. Sixty-four young adults were recruited to participate in two experiments (Experiment 1: n = 36; Experiment 2: n = 28) using a VSTM orientation change-detection task under TMS. Monocular vision was ensured using red-blue goggles combined with red-blue stimuli. Double-pulse TMS was delivered at different times (Experiment 1: 0, 200 or 1000 ms; Experiment 2: 200, 1000 ms) during a 2 s maintenance phase, on one side of the occipital hemisphere. In Experiment 2, a sham TMS condition was introduced. Decreased detection sensitivity (d') in the ipsilateral occipital hemisphere to visual hemifield, and in the real TMS (compared with sham TMS) condition indicated inhibitory TMS effects, and thus, a causal involvement of the sensory visual cortex during early (200 ms) and late (1000 ms) maintenance in VSTM. These findings are aligned with sensory recruitment, which proposes that both perceptual and memory processes rely upon the same neural substrates in the sensory visual cortex. The methods used in this study were preregistered and had received in-principle acceptance on 6 June 2022 (Stage 1 protocol can be found in: https://doi.org/10.17605/OSF.IO/EMPDT).

Bridging the gap between believing and memory functions

Believing has recently been recognized as a fundamental brain function linking a person’s experience with his or her attitude, actions and predictions. In general, believing results from the integration of ambient information with emotions and can be reinforced or modulated in a probabilistic fashion by new experiences. Although these processes occur in the subliminal realm, humans can become aware of what they believe and express it verbally. We explain how believing is interwoven with memory functions in a multifaceted fashion. Linking the typically rapid and adequate reactions of a subject to what he/she believes is enabled by working memory. Perceptions are stored in episodic memory as beneficial or aversive events, while the corresponding verbal descriptions of what somebody believes are stored in semantic memory. After recall from memory of what someone believes, personally relevant information can be communicated to other people. Thus, memory is essential for maintaining what people believe.

Temporal expectation based on the duration variability modulates alpha oscillations during working memory retention

While maintaining information over a delay of time, working memory (WM) also allows individuals to prepare the mnemonic contents for prospective utilisation. However, it remains unclear whether the expectation of the time of WM test could modulate neural responses during the retention interval of WM and subsequent performance. Here, we investigated whether temporal expectations based on the variability of delay duration can modulate 9-13 Hz alpha oscillations during WM retention and whether the expectation-induced alpha activity was associated with WM performance. Participants performed a retro-cueing WM task with magnetoencephalography (MEG) (Experiment 1) and a standard WM task with electroencephalography (EEG) (Experiment 2). The expectation of the timing of the WM test was manipulated by the temporal structure of the tasks with small or large variability in the delay durations. We showed that alpha oscillations during retention interval and WM performance varied with duration variability in both of the MEG and EEG experiments. The novel finding was greater alpha-power attenuation over the left frontal and parietal regions during WM retention when the duration variability was small and the test onset was predictable, compared to when the duration variability was large and the test onset was less predictable. Importantly, we observed a positive relationship in variability difference between the response benefit and alpha-power attenuation in the left posterior parietal regions at both MEG-source and EEG-electrode levels. Finally, we confirmed the behavioural benefit when a condition with a fixed delay-duration was included in a behavioural experiment (Experiment 3). When conjoined, the delay duration enables individuals to anticipate when the relevant information would be put to work, and alpha oscillations track the anticipatory states during WM maintenance.

Robust and brain-like working memory through short-term synaptic plasticity

Working memory has long been thought to arise from sustained spiking/attractor dynamics. However, recent work has suggested that short-term synaptic plasticity (STSP) may help maintain attractor states over gaps in time with little or no spiking. To determine if STSP endows additional functional advantages, we trained artificial recurrent neural networks (RNNs) with and without STSP to perform an object working memory task. We found that RNNs with and without STSP were able to maintain memories despite distractors presented in the middle of the memory delay. However, RNNs with STSP showed activity that was similar to that seen in the cortex of a non-human primate (NHP) performing the same task. By contrast, RNNs without STSP showed activity that was less brain-like. Further, RNNs with STSP were more robust to network degradation than RNNs without STSP. These results show that STSP can not only help maintain working memories, it also makes neural networks more robust and brain-like.

Sensory recruitment in visual short-term memory: A systematic review and meta-analysis of sensory visual cortex interference using transcranial magnetic stimulation

Sensory visual areas are involved in encoding information in visual short-term memory (VSTM). Yet it remains unclear whether sensory visual cortex is a necessary component of the brain network for maintenance of information in VSTM. Here, we aimed to systematically review studies that have investigated the role of the sensory visual cortex in VSTM using transcranial magnetic stimulation (TMS) and to quantitatively explore these effects using meta-analyses. Fourteen studies were identified and reviewed. Eight studies provided sufficient data for meta-analysis. Two meta-analyses, one regarding the VSTM encoding phase (17 effect sizes) and one regarding the VSTM maintenance phase (15 effect sizes), two meta-regressions (32 effect sizes in each), and one exploratory meta-analysis were conducted. Our results indicate that the sensory visual cortex is similarly involved in both the encoding and maintenance VSTM phase. We suggest that some cases where evidence did not show significant TMS effects was due to low memory or perceptual task demands. Overall, these findings support the idea that sensory visual areas are part of the brain network responsible for successfully maintaining information in VSTM.

Differential gene expression in layer 3 pyramidal neurons across 3 regions of the human cortical visual spatial working memory network

Visual spatial working memory (vsWM) is mediated by a distributed cortical network composed of multiple nodes, including primary visual (V1), posterior parietal (PPC), and dorsolateral prefrontal (DLPFC) cortices. Feedforward and feedback information is transferred among these nodes via projections furnished by pyramidal neurons (PNs) located primarily in cortical layer 3. Morphological and electrophysiological differences among layer 3 PNs across these nodes have been reported; however, the transcriptional signatures underlying these differences have not been examined in the human brain. Here we interrogated the transcriptomes of layer 3 PNs from 39 neurotypical human subjects across 3 critical nodes of the vsWM network. Over 8,000 differentially expressed genes were detected, with more than 6,000 transcriptional differences present between layer 3 PNs in V1 and those in PPC and DLPFC. Additionally, over 600 other genes differed in expression along the rostral-to-caudal hierarchy formed by these 3 nodes. Moreover, pathway analysis revealed enrichment of genes in V1 related to circadian rhythms and in DLPFC of genes involved in synaptic plasticity. Overall, these results show robust regional differences in the transcriptome of layer 3 PNs, which likely contribute to regional specialization in their morphological and physiological features and thus in their functional contributions to vsWM.

Shielding working-memory representations from temporally predictable external interference

Protecting working-memory content from distracting external sensory inputs and intervening tasks is an ubiquitous demand in daily life. Here, we ask whether and how temporal expectations about external events can help mitigate effects of such interference during working-memory retention. We manipulated the temporal predictability of interfering items that occurred during the retention period of a visual working-memory task and report that temporal expectations reduce the detrimental influence of external interference on subsequent memory performance. Moreover, to determine if the protective effects of temporal expectations rely on distractor suppression or involve shielding of internal representations, we compared effects after irrelevant distractors that could be ignored vs. interrupters that required a response. Whereas distractor suppression may be sufficient to confer protection from predictable distractors, any benefits after interruption are likely to involve memory shielding. We found similar benefits of temporal expectations after both types of interference. We conclude that temporal expectations may play an important role in safeguarding behaviour based on working memory - acting through mechanisms that include the shielding of internal content from external interference.

Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories

Persistently active neurons during mnemonic periods have been regarded as the mechanism underlying working memory maintenance. Alternatively, neuronal networks could instead store memories in fast synaptic changes, thus avoiding the biological cost of maintaining an active code through persistent neuronal firing. Such "activity-silent" codes have been proposed for specific conditions in which memories are maintained in a nonprioritized state, as for unattended but still relevant short-term memories. A hallmark of this "activity-silent" code is that these memories can be reactivated from silent, synaptic traces. Evidence for "activity-silent" working memory storage has come from human electroencephalography (EEG), in particular from the emergence of decodability (EEG reactivations) induced by visual impulses (termed pinging) during otherwise "silent" periods. Here, we reanalyze EEG data from such pinging studies. We find that the originally reported absence of memory decoding reflects weak statistical power, as decoding is possible based on more powered analyses or reanalysis using alpha power instead of raw voltage. This reveals that visual pinging EEG "reactivations" occur in the presence of an electrically active, not silent, code for unattended memories in these data. This crucial change in the evidence provided by this dataset prompts a reinterpretation of the mechanisms of EEG reactivations. We provide 2 possible explanations backed by computational models, and we discuss the relationship with TMS-induced EEG reactivations.

A dynamic neural field model of continuous input integration

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a "bump" independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.

The Imbalanced Plasticity Hypothesis of Schizophrenia-Related Psychosis: A Predictive Perspective

A considerable number of studies have attempted to account for the psychotic aspects of schizophrenia in terms of the influential predictive coding (PC) hypothesis. We argue that the prediction-oriented perspective on schizophrenia-related psychosis may benefit from a mechanistic model that: 1) gives due weight to the extent to which alterations in short- and long-term synaptic plasticity determine the degree and the direction of the functional disruption that occurs in psychosis; and 2) addresses the distinction between the two central syndromes of psychosis in schizophrenia: disorganization and reality-distortion. To accomplish these goals, we propose the Imbalanced Plasticity Hypothesis - IPH, and demonstrate that it: 1) accounts for commonalities and differences between disorganization and reality distortion in terms of excessive (hyper) or insufficient (hypo) neuroplasticity, respectively; 2) provides distinct predictions in the cognitive and electrophysiological domains; and 3) is able to reconcile conflicting PC-oriented accounts of psychosis.

Distinct Neural Representations of Content and Ordinal Structure in Auditory Sequence Memory

Two forms of information, frequency (content) and ordinal position (structure), have to be stored when retaining a sequence of auditory tones in working memory (WM). However, the neural representations and coding characteristics of content and structure, particularly during WM maintenance, remain elusive. Here, in two EEG studies in human participants (both sexes), by transiently perturbing the "activity-silent" WM retention state and decoding the reactivated WM information, we demonstrate that content and structure are stored in a dissociative manner with distinct characteristics throughout WM process. First, each tone in the sequence is associated with two codes in parallel, characterizing its frequency and ordinal position, respectively. Second, during retention, a structural retrocue successfully reactivates structure but not content, whereas a following white noise triggers content but not structure. Third, structure representation remains stable, whereas content code undergoes a dynamic transformation through memory progress. Finally, the noise-triggered content reactivations during retention correlate with subsequent WM behavior. Overall, our results support distinct content and structure representations in auditory WM and provide an efficient approach to access the silently stored WM information in the human brain. The dissociation of content and structure could facilitate efficient memory formation via generalizing stable structure to new auditory contents.SIGNIFICANCE STATEMENT In memory experiences, contents do not exist independently but are linked with each other via ordinal structure. For instance, recalling a piece of favorite music relies on correct ordering (sequence structure) of musical tones (content). How are the structure and content for an auditory temporally structured experience maintained in working memory? Here, by using impulse-response approach and time-resolved representational dissimilarity analysis on human EEG recordings in an auditory working memory task, we reveal that content and structure are stored in a dissociated way, which would facilitate efficient and rapid memory formation through generalizing stable structure knowledge to new auditory inputs.

Perceptual Learning beyond Perception: Mnemonic Representation in Early Visual Cortex and Intraparietal Sulcus

The ability to discriminate between stimuli relies on a chain of neural operations associated with perception, memory and decision-making. Accumulating studies show learning-dependent plasticity in perception or decision-making, yet whether perceptual learning modifies mnemonic processing remains unclear. Here, we trained human participants of both sexes in an orientation discrimination task, while using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to separately examine training-induced changes in working memory (WM) representation. fMRI decoding revealed orientation-specific neural patterns during the delay period in primary visual cortex (V1) before, but not after, training, whereas neurodisruption of V1 during the delay period led to behavioral deficits in both phases. In contrast, both fMRI decoding and disruptive effect of TMS showed that intraparietal sulcus (IPS) represented WM content after, but not before, training. These results suggest that training does not affect the necessity of sensory area in representing WM information, consistent with the sensory recruitment hypothesis in WM, but likely alters the coding format of the stored stimulus in this region. On the other hand, training can render WM content to be maintained in higher-order parietal areas, complementing sensory area to support more robust maintenance of information.SIGNIFICANCE STATEMENT There has been accumulating progresses regarding experience-dependent plasticity in perception or decision-making, yet how perceptual experience moulds mnemonic processing of visual information remains less explored. Here, we provide novel findings that learning-dependent improvement of discriminability accompanies altered WM representation at different cortical levels. Critically, we suggest a role of training in modulating cortical locus of WM representation, providing a plausible explanation to reconcile the discrepant findings between human and animal studies regarding the recruitment of sensory or higher-order areas in WM.

Enhanced synaptic properties of the prefrontal cortex and hippocampus after learning a spatial working memory task in adult male mice

Working memory (WM) is the ability to hold on-line and manipulate information. The prefrontal cortex (PFC) is a key brain region involved in WM, while the hippocampus is also involved, particularly, in spatial WM. Although several studies have investigated the neuronal substrates of WM in trained animals, the effects and the mechanisms underlying learning WM tasks have not been explored. In our study, we investigated the effects of learning WM tasks in mice on the function of PFC and hippocampus, by training mice in the delayed alternation task for 9 days (adaptive group). This group was compared to naïve mice (which stayed in their homecage) and mice trained in the alternation procedure only (non-adaptive). Following training, a cohort of mice (Experiment A) was tested in the left-right discrimination task and the reversal learning task, while another cohort (Experiment B) was tested in the attention set-shifting task (AST). The adaptive group performed significantly better in the reversal learning task (Experiment A) and AST (Experiment B), compared to non-adaptive and naïve groups. At the end of the behavioral experiments in Experiment A, field excitatory post-synaptic potential (fEPSP) recordings were performed in PFC and hippocampal brain slices. The adaptive group had enhanced the long-term potentiation (LTP) in the PFC, compared to the other groups. In the hippocampus, both the adaptive and the non-adaptive groups exhibited increased fEPSP compared to the naïve group, but no differences in LTP. In Experiment B, the dendritic spine density was measured, which, in the PFC, was found increased in the adaptive group, compared to the non-adaptive and naïve groups. In the hippocampus, there was an increase in mature dendritic spine density in the adaptive group, compared to the other two groups. Our results indicate a role for LTP and dendritic spine density in learning WM tasks.

Attention effects in working memory that are asymmetric across sensory modalities

A key unanswered question about working memory is the nature of interference between items. At one extreme of existing theories, interference occurs between any two items because of a general capacity limit. At another extreme, interference depends on the similarity between particular features of different items. We examine this question in three experiments by presenting two sets of items on each trial, comprising tones or colors, with three levels of similarity between the two sets: cross-modal, unimodal with different marking features (two different musical instruments or shapes), and unimodal with the same marking feature. Another question is the extent to which the entry of presented items into working memory is obligatory or optional, which we examined by requiring retention of the first, the second, or both sets of stimuli for a recognition test shortly after the presentation of the two sets. The combination of the set similarity and attention manipulations allows us to draw conclusions about the nature of working-memory storage. The findings were not entirely in accord with any pre-existing theory. The effects of feature similarity were present in both modalities but more pronounced for sounds, whereas the detrimental effects of attention to both sets for retention occurred only for visual stimuli. Based on the findings we suggest a new, hybrid conception of working memory storage.

Distraction in Visual Working Memory: Resistance is Not Futile

Over half a century of research focused on understanding how working memory is capacity constrained has overshadowed the fact that it is also remarkably resistant to interference. Protecting goal-relevant information from distraction is a cornerstone of cognitive function that involves a multifaceted collection of control processes and storage mechanisms. Here, we discuss recent advances in cognitive psychology and neuroscience that have produced new insights into the nature of visual working memory and its ability to resist distraction. We propose that distraction resistance should be an explicit component in any model of working memory and that understanding its behavioral and neural correlates is essential for building a comprehensive understanding of real-world memory function.

Temporally coherent perturbation of neural dynamics during retention alters human multi-item working memory

Temporarily storing a list of items in working memory (WM), a fundamental ability in cognition, has been posited to rely on the temporal dynamics of multi-item neural representations during retention. However, the causal evidence, particularly in human subjects, is still lacking, let alone WM manipulation. Here, we develop a novel "dynamic perturbation" approach to manipulate the relative memory strength of WM items held in human brain, by presenting temporally correlated luminance sequences during retention to interfere with the multi-item neural dynamics. Six experiments on more than 150 subjects confirm the effectiveness of this WM manipulation approach. A computational model combining continuous attractor neural network (CANN) and short-term synaptic plasticity (STP) principles further reproduces all the empirical findings. The model reveals that the "dynamic perturbation" modifies the synaptic efficacies of WM items through STP principles, eventually leading to changes in their relative memory strengths. Our results support the causal role of temporal dynamics of neural network in mediating multi-item WM, and offer a promising, purely bottom-up approach to manipulate WM.

Is Activity Silent Working Memory Simply Episodic Memory?

Working memory (WM) maintains task-relevant information in a state ready for processing. While traditional theories assume that sustained neuronal activity is responsible for WM, the Activity Silent WM (ASWM) account proposes that maintenance can also be supported by short-term synaptic weight changes. Here, we argue that the evidence for ASWM can be explained more parsimoniously by the involvement of episodic memory (EM) in WM tasks. Like ASWM, EM relies on rapid synaptic modification that is also activity silent; however, while ASWM posits transient synaptic modifications, EM traces persist over longer time periods. We discuss how, despite this difference, well-established EM mechanisms can account for the key findings attributed to ASWM, and describe predictions of this account.

If deep learning is the answer, what is the question?

Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This approach has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, and not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterize computations or neural codes, or who wish to understand perception, attention, memory and executive functions? In this Perspective, our goal is to offer a road map for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics and neural representations in artificial and biological systems, and we highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.

Vesicle Pools of Memory at Mossy Fiber Synapses

In this issue of Neuron, Vandael et al. (2020) reveal that post-tetanic potentiation at dentate gyrus mossy fiber synapses is induced by natural activity patterns. This plasticity is mediated by an increase in readily releasable vesicle pool size and is extended in the absence of activity, forming a "pool engram."

Theoretical distinction between functional states in working memory and their corresponding neural states

Working memory (WM) is important for guiding behaviour, but not always for the next possible action. 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 defined as 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, functionally active and latent cognitive states of WM have been associated with storage based on persistent-activity and activity-silent neural mechanisms, respectively. However, 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-based and activity-silent forms of WM storage.

Revisit once more the sensory storage account of visual working memory

Recent work has highlighted the role of early visual areas in visual working memory (VWM) storage and put forward a sensory storage account of VWM. Using a distractor interference paradigm, however, we previolsy showed that the contribution of early visual areas to VWM storage may not be essential. Instead, higher cortical regions such as the posterior parietal cortex may play a more significant role in VWM storage. This is consistent with reviews of other available behavioral, neuroimaging and neurophysiology results. Recently, a number of studies brought forward new evidence regarding this debate. Here I review these new pieces of evidence in detail and show that there is still no strong and definitive evidence supporting an essential role of the early visual areas in VWM storage. Instead, converging evidence suggests that early visual areas may contribute to the decision stage of a VWM task by facilitating target and probe comparison. Aside from further clarifying this debate, it is also important to note that whether or not VWM storage uses a sensory code depends on how it is defined, and that behavioral interactions between VWM and perception tasks do not necessarily support the involvement of sensory regions in VWM storage.