From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization

A computational approach to the N-back task

The N-back task is one of the most popular paradigms for studying the cognitive mechanisms of working memory (WM). The task requires the observer to view a sequence of stimuli and judge whether the current stimulus (probe) matches the one presented N stimuli ago (target). A key phenomenon is that the intervening stimuli (distractors) interfere with task performance. Unfortunately, the classic N-back task uses complex categorical stimuli, making it unfit to quantify the effect of feature similarity on interference strength. Here, we introduce the "analog N-back task", which utilizes stimuli varying continuously in orientation or color. This task variant enables us to measure interference strength on a continuum, providing data suitable for identifying the sources of interference using computational models. In the analog 2-back task, we found that interference increased with feature similarity between the probe and both task-relevant (1-back) and task-irrelevant (3-back) distractors. We next developed and evaluated three main models that each incorporated a Bayesian decision step and differed from an optimal non-interference model in one component only: an early-pooling model, a late-pooling model, and a substitution model. Model comparison suggests that interference emerges late in processing, most likely due to confusion between stimuli during WM retrieval. Our work puts the study of interference in the N-back task on a firmer computational footing and provides a unified framework for examining the sources of interference across domains.

Adaptive chunking improves effective working memory capacity in a prefrontal cortex and basal ganglia circuit

How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such "chunking" strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinson's disease, ADHD and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.

Evidence for the Beneficial Effect of Reward on Working Memory: A Meta-Analytic Study

Rewards act as external motivators and can improve performance in various cognitive tasks. However, previous research demonstrated mixed findings regarding the effect of reward on working memory (WM) performance, and the question of whether reward enhances WM performance is arguable. It remains unclear how the effect of reward on WM can be influenced by various factors, such as types of reward and experimental paradigms. In this meta-analytic study, we systematically investigated the effect of reward on WM by analyzing data from 51 eligible studies involving a total of 1767 participants. Our results showed that reward robustly enhanced WM performance, with non-monetary rewards inducing more benefits than monetary rewards. This may be because, while both types of reward could induce extrinsic motivation, non-monetary rewards enhanced intrinsic motivation while monetary rewards reduced it. Notably, all three reward methods-reward binding, reward expectation, and subliminal reward-effectively improved WM performance, with the reward binding paradigm exhibiting the greatest effects. This finding suggests that the reward effect can be attributed to both increasing the total amount of WM resources and improving the flexibility of resource reallocation. Moreover, the type of WM, the experimental paradigms, and the outcome measures are three moderators that should be jointly considered when assessing the reward effects on WM. Overall, this meta-analytic study provides solid evidence that reward improves WM performance and reveals possible mechanisms underlying these improvements.

Representation and computation in visual working memory

The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.

γ And β Band Oscillation in Working Memory Given Sequential or Concurrent Multiple Items: A Spiking Network Model

Working memory (WM) can maintain sequential and concurrent information, and the load enhances the γ band oscillation during the delay period. To provide a unified account for these phenomena in working memory, we investigated a continuous network model consisting of pyramidal cells, high-threshold fast-spiking interneurons (FS), and low-threshold nonfast-spiking interneurons (nFS) for working memory of sequential and concurrent directional cues. Our model exhibits the γ (30-100 Hz) and β (10-30 Hz) band oscillation during the retention of both concurrent cues and sequential cues. We found that the β oscillation results from the interaction between pyramidal cells and nFS, whereas the γ oscillation emerges from the interaction between pyramidal cells and FS because of the strong excitation elicited by cue presentation, shedding light on the mechanism underlying the enhancement of γ power in many cognitive executions.

A "Bandwidth" in cortical representations of multiple faces

The human ability to process multiple items simultaneously can be constrained by the extent to which those items are represented by distinct neural populations. In the current study, we used fMRI to investigate the cortical representation of multiple faces. We found that the addition of a second face to occupy both visual hemifields led to an increased response, whereas a further addition of faces within the same visual hemifield resulted in a decreased response. This pattern was widely observed in the occipital visual cortex, the intraparietal sulcus, and extended to the posterior inferotemporal cortex. A parallel trend was found in a behavioral change-detection task, revealing a perceptual "bandwidth" of multiface processing. The sensitivity to face clutter gradually decreased along the ventral pathway, supporting the notion of a buildup of clutter-tolerance representation. These cortical response patterns to face clutters suggest that adding signals with nonoverlapping cortical representation enhanced perception, while adding signals that competed for representation resources impaired perception.

Reward influences the allocation but not the availability of resources in visual working memory

Visual working memory possesses capacity constraints limiting the availability of resources for encoding and maintaining information. Studies have shown that prospective rewards improve performance on visual working memory tasks, but it remains unclear whether rewards increase total resource availability or simply influence the allocation of resources. Participants performed a continuous report visual working memory task with oriented grating stimuli. On each trial, participants were presented with a priority cue, which signaled the item most likely to be probed, and a reward cue, which signaled the magnitude of a performance-contingent reward. We showed that rewards decreased recall error for cued items and increased recall error for noncued items. This tradeoff was due to a change in the probability of successfully encoding a cued versus a noncued item rather than a change in recall precision or the probability of binding errors. Rewards did not modulate performance when priority cues were retroactively presented after the stimulus presentation period, indicating that rewards only affect resource allocation when participants are able to engage proactive control before encoding. Additionally, reward had no effect on visual working memory performance when priority cues were absent and thus unable to guide resource allocation. These findings indicate that rewards influence the flexible allocation of resources during selection and encoding in visual working memory, but do not augment total capacity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multiple and Dissociable Effects of Sensory History on Working-Memory Performance

Behavioral reports of sensory information are biased by stimulus history. The nature and direction of such serial-dependence biases can differ between experimental settings; both attractive and repulsive biases toward previous stimuli have been observed. How and when these biases arise in the human brain remains largely unexplored. They could occur either via a change in sensory processing itself and/or during postperceptual processes such as maintenance or decision-making. To address this, we tested 20 participants (11 female) and analyzed behavioral and magnetoencephalographic (MEG) data from a working-memory task in which participants were sequentially presented with two randomly oriented gratings, one of which was cued for recall at the end of the trial. Behavioral responses showed evidence for two distinct biases: (1) a within-trial repulsive bias away from the previously encoded orientation on the same trial, and (2) a between-trial attractive bias toward the task-relevant orientation on the previous trial. Multivariate classification of stimulus orientation revealed that neural representations during stimulus encoding were biased away from the previous grating orientation, regardless of whether we considered the within-trial or between-trial prior orientation, despite opposite effects on behavior. These results suggest that repulsive biases occur at the level of sensory processing and can be overridden at postperceptual stages to result in attractive biases in behavior.SIGNIFICANCE STATEMENT Recent experience biases behavioral reports of sensory information, possibly capitalizing on the temporal regularity in our environment. It is still unclear at what stage of stimulus processing such serial biases arise. Here, we recorded behavior and neurophysiological [magnetoencephalographic (MEG)] data to test whether neural activity patterns during early sensory processing show the same biases seen in participants' reports. In a working-memory task that produced multiple biases in behavior, responses were biased toward previous targets, but away from more recent stimuli. Neural activity patterns were uniformly biased away from all previously relevant items. Our results contradict proposals that all serial biases arise at an early sensory processing stage. Instead, neural activity exhibited mostly adaptation-like responses to recent stimuli.

A spiking network model for clustering report in a visual working memory task

Introduction

Working memory (WM) plays a key role in many cognitive processes, and great interest has been attracted by WM for many decades. Recently, it has been observed that the reports of the memorized color sampled from a uniform distribution are clustered, and the report error for the stimulus follows a Gaussian distribution.

Methods

Based on the well-established ring model for visuospatial WM, we constructed a spiking network model with heterogeneous connectivity and embedded short-term plasticity (STP) to investigate the neurodynamic mechanisms behind this interesting phenomenon.

Results

As a result, our model reproduced the clustering report given stimuli sampled from a uniform distribution and the error of the report following a Gaussian distribution. Perturbation studies showed that the heterogeneity of connectivity and STP are necessary to explain experimental observations.

Conclusion

Our model provides a new perspective on the phenomenon of visual WM in experiments.

Synthesizing network dynamics for short-term memory of impulsive inputs

Illuminating the mechanisms that the brain uses to manage and coordinate its resources is a core question in neuroscience. In particular, circuits and networks in the brain are able to encode, store and recall large amounts of information, in the service of a wide range of functionality. How do the various dynamical mechanisms within these networks allow for such coordination? We consider the specific problem of how the dynamics of networks can enact a representation of input stimuli that is retained over time, i.e., a form of short-term memory. We utilize modeling and control-theoretic methods to approach these questions, treating the state trajectory of a dynamical system as an abstract memory trace of prior inputs. The inputs impinge on the network via a variable gain, which is to be synthesized by optimization. In order to perpetuate these memory traces of stimuli, we propose that this gain is adapted to optimize: i) the error between a ground truth representation of stimuli and the encoding of them; as well as ii) overwriting of prior information. Optimizing over these central tenets of memory, we obtain a 'policy' for adapting the input gain that is dependent on the state of the network. This derived policy yields a recurrent neural network between the policy and the neural circuits, affirming existing theories that the prefrontal cortex may hold subnetworks dedicated to working memory while actively engaging with other neural subnetworks.

Neural interactions in working memory explain decreased recall precision and similarity-based feature repulsion

Over the last several years, the study of working memory (WM) for simple visual features (e.g., colors, orientations) has been dominated by perspectives that assume items in WM are stored independently of one another. Evidence has revealed, however, systematic biases in WM recall which suggest that items in WM interact during active maintenance. In the present study, we report two experiments that replicate a repulsion bias between metrically similar colors during active storage in WM. We also observed that metrically similar colors were stored with lower resolution than a unique color held actively in mind at the same time. To account for these effects, we report quantitative simulations of two novel neurodynamical models of WM. In both models, the unique behavioral signatures reported here emerge directly from laterally-inhibitory neural interactions that serve to maintain multiple, distinct neural representations throughout the WM delay period. Simulation results show that the full pattern of empirical findings was only obtained with a model that included an elaborated spatial pathway with sequential encoding of memory display items. We discuss implications of our findings for theories of visual working memory more generally.

Multiple bumps can enhance robustness to noise in continuous attractor networks

A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells, which maintain periodic representations of position and orientation, respectively. These representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We address this knowledge gap by constructing 1D ring attractor networks with different bump numbers and characterizing their responses to three types of noise: fluctuating inputs, spiking noise, and deviations in connectivity away from ideal attractor configurations. Across all three types, networks with more bumps experience less noise-driven deviations in bump motion. This translates to more robust encodings of linear coordinates, like position, assuming that each neuron represents a fixed length no matter the bump number. Alternatively, we consider encoding a circular coordinate, like orientation, such that the network distance between adjacent bumps always maps onto 360 degrees. Under this mapping, bump number does not significantly affect the amount of error in the coordinate readout. Our simulation results are intuitively explained and quantitatively matched by a unified theory for path integration and noise in multi-bump networks. Thus, to suppress the effects of biologically relevant noise, continuous attractor networks can employ more bumps when encoding linear coordinates; this advantage disappears when encoding circular coordinates. Our findings provide motivation for multiple bumps in the mammalian grid network.

An adaptive perspective on visual working memory distortions

When holding multiple items in visual working memory, representations of individual items are often attracted to, or repelled from, each other. While this is empirically well-established, existing frameworks do not account for both types of distortions, which appear to be in opposition. Here, we demonstrate that both types of memory distortion may confer functional benefits under different circumstances. When there are many items to remember and subjects are near their capacity to accurately remember each item individually, memories for each item become more similar (attraction). However, when remembering smaller sets of highly similar but discernible items, memory for each item becomes more distinct (repulsion), possibly to support better discrimination. Importantly, this repulsion grows stronger with longer delays, suggesting that it dynamically evolves in memory and is not just a differentiation process that occurs during encoding. Furthermore, both attraction and repulsion occur even in tasks designed to mitigate response bias concerns, suggesting they are genuine changes in memory representations. Together, these results are in line with the theory that attraction biases act to stabilize memory signals by capitalizing on information about an entire group of items, whereas repulsion biases reflect a tradeoff between maintaining accurate but distinct representations. Both biases suggest that human memory systems may sacrifice veridical representations in favor of representations that better support specific behavioral goals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions

The development of treatments for impaired cognition in schizophrenia has been characterized as the most important challenge facing psychiatry at the beginning of the twenty-first century. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) project was designed to build on the potential benefits of using tasks and tools from cognitive neuroscience to better understanding and treat cognitive impairments in psychosis. These benefits include: (1) the use of fine-grained tasks that measure discrete cognitive processes; (2) the ability to design tasks that distinguish between specific cognitive domain deficits and poor performance due to generalized deficits resulting from sedation, low motivation, poor test taking skills, etc.; and (3) the ability to link cognitive deficits to specific neural systems, using animal models, neuropsychology, and functional imaging. CNTRICS convened a series of meetings to identify paradigms from cognitive neuroscience that maximize these benefits and identified the steps need for translation into use in clinical populations. The Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia (CNTRaCS) Consortium was developed to help carry out these steps. CNTRaCS consists of investigators at five different sites across the country with diverse expertise relevant to a wide range of the cognitive systems identified as critical as part of CNTRICs. This work reports on the progress and current directions in the evaluation and optimization carried out by CNTRaCS of the tasks identified as part of the original CNTRICs process, as well as subsequent extensions into the Positive Valence systems domain of Research Domain Criteria (RDoC). We also describe the current focus of CNTRaCS, which involves taking a computational psychiatry approach to measuring cognitive and motivational function across the spectrum of psychosis. Specifically, the current iteration of CNTRaCS is using computational modeling to isolate parameters reflecting potentially more specific cognitive and visual processes that may provide greater interpretability in understanding shared and distinct impairments across psychiatric disorders.

Strategy-dependent effects of working-memory limitations on human perceptual decision-making

Deliberative decisions based on an accumulation of evidence over time depend on working memory, and working memory has limitations, but how these limitations affect deliberative decision-making is not understood. We used human psychophysics to assess the impact of working-memory limitations on the fidelity of a continuous decision variable. Participants decided the average location of multiple visual targets. This computed, continuous decision variable degraded with time and capacity in a manner that depended critically on the strategy used to form the decision variable. This dependence reflected whether the decision variable was computed either: (1) immediately upon observing the evidence, and thus stored as a single value in memory; or (2) at the time of the report, and thus stored as multiple values in memory. These results provide important constraints on how the brain computes and maintains temporally dynamic decision variables.

Working memory, the brain’s ability to temporarily store and recall information, is a critical part of decision making – but it has its limits. The brain can only store so much information, for so long. Since decisions are not often acted on immediately, information held in working memory ‘degrades’ over time. However, it is unknown whether or not this degradation of information over time affects the accuracy of later decisions.

The tactics that people use, knowingly or otherwise, to store information in working memory also remain unclear. Do people store pieces of information such as numbers, objects and particular details? Or do they tend to compute that information, make some preliminary judgement and recall their verdict later? Does the strategy chosen impact people’s decision-making?

To investigate, Schapiro et al. devised a series of experiments to test whether the limitations of working memory, and how people store information, affect the accuracy of decisions they make. First, participants were shown an array of colored discs on a screen. Then, either immediately after seeing the disks or a few seconds later, the participants were asked to recall the position of one of the disks they had seen, or the average position of all the disks. This measured how much information degraded for a decision based on multiple items, and how much for a decision based on a single item. From this, the method of information storage used to make a decision could be inferred.

Schapiro et al. found that the accuracy of people’s responses worsened over time, whether they remembered the position of each individual disk, or computed their average location before responding. The greater the delay between seeing the disks and reporting their location, the less accurate people’s responses tended to be. Similarly, the more disks a participant saw, the less accurate their response became. This suggests that however people store information, if working memory reaches capacity, decision-making suffers and that, over time, stored information decays.

Schapiro et al. also noticed that participants remembered location information in different ways depending on the task and how many disks they were shown at once. This suggests people adopt different strategies to retain information momentarily.

In summary, these findings help to explain how people process and store information to make decisions and how the limitations of working memory impact their decision-making ability. A better understanding of how people use working memory to make decisions may also shed light on situations or brain conditions where decision-making is impaired.

An interference model for visual working memory: Applications to the change detection task

Most studies of visual-working memory employ one of two experimental paradigms: change-detection or continuous-stimulus reproduction. In this study, we extended the Interference Model (IM; Oberauer & Lin, 2017), which was designed for continuous reproduction, to the single-probe change-detection task. In continuous reproduction, participants occasionally report the non-target items instead of the target. The presence of non-target response is predicted by the Interference Model, which relies in part on the interference of non-target items to explain the set-size effect. By presenting a probe matching a non-target item, we can investigate the amount of interference from non-target items in change detection. As predicted by the Interference Model, we observed poorer performance in rejecting a probe matching a non-target item compared to a new probe (i.e., a cost due to intrusions from non-targets). We fitted the IM along with the Variable Precision, the Slot-Averaging, and the Neural-Population model to the data from two change-detection experiments. The models were equipped with a Bayesian decision rule based on the one used in Keshvari, van den Berg, and Ma (2013). The Interference Model and the Neural-Population model successfully predicted the set-size effect and the non-target intrusion cost, whereas the Variable Precision (VP) and Slot-Averaging (SA) models failed to predict the intrusion cost at all. Even with additional assumptions enabling VP and SA to produce intrusion costs, the IM still performed better than the competing models quantitatively.

Active Working Memory and Simple Cognitive Operations

Working memory is thought to serve as a buffer for ongoing cognitive operations, even in tasks that have no obvious memory requirements. This conceptualization has been supported by dual-task experiments, in which interference is observed between a primary task involving short-term memory storage and a secondary task that presumably requires the same buffer as the primary task. Little or no interference is typically observed when the secondary task is very simple. Here, we test the hypothesis that even very simple tasks require the working memory buffer, but interference can be minimized by using activity-silent representations to store the information from the primary task. We tested this hypothesis using dual-task paradigm in which a simple discrimination task was interposed in the retention interval of a change detection task. We used contralateral delay activity (CDA) to track the active maintenance of information for the change detection task. We found that the CDA was massively disrupted after the interposed task. Despite this disruption of active maintenance, we found that performance in the change detection task was only slightly impaired, suggesting that activity-silent representations were used to retain the information for the change detection task. A second experiment replicated this result and also showed that automated discriminations could be performed without producing a large CDA disruption. Together, these results suggest that simple but non-automated discrimination tasks require the same processes that underlie active maintenance of information in working memory.

Towards biologically constrained attractor models of schizophrenia

Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing alterations in memory circuits can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing biophysical accounts. Critical points to resolve are the effects of increases vs. decreases in E/I ratio (e.g. through NMDAR blockade) on firing rate tuning and shared noise modulations and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.

Biophysical mechanism of the interaction between default mode network and working memory network

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09674-1.

50 years of mnemonic persistent activity: quo vadis?

Half a century ago persistent spiking activity in the neocortex was discovered to be a neural substrate of working memory. Since then scientists have sought to understand this core cognitive function across biological and computational levels. Studies are reviewed here that cumulatively lend support to a synaptic theory of recurrent circuits for mnemonic persistent activity that depends on various cellular and network substrates and is mathematically described by a multiple-attractor network model. Crucially, a mnemonic attractor state of the brain is consistent with temporal variations and heterogeneity across neurons in a subspace of population activity. Persistent activity should be broadly understood as a contrast to decaying transients. Mechanisms in the absence of neural firing ('activity-silent state') are suitable for passive short-term memory but not for working memory - which is characterized by executive control for filtering out distractors, limited capacity, and internal manipulation of information.

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.

Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory

Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relative to a non-target stimulus, reflecting the failure to maintain the appropriate association or "binding" between the features that define one object (e.g., color and location). The mechanisms underlying feature binding in working memory remain unknown. Here, we tested the hypothesis that features are bound in memory through synchrony across feature-specific neural assemblies. We built a biophysical neural network model composed of two one-dimensional attractor networks - one for color and one for location - simulating feature storage in different cortical areas. Within each area, gamma oscillations were induced during bump attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. As a result, different memorized items were held at different phases of the network's oscillation. These two areas were then reciprocally connected via weak cortico-cortical excitation, accomplishing binding between color and location through the synchronization of pairs of bumps across the two areas. Encoding and decoding of color-location associations was accomplished through rate coding, overcoming a long-standing limitation of binding through synchrony. In some simulations, swap errors arose: "color bumps" abruptly changed their phase relationship with "location bumps." This model, which leverages the explanatory power of similar attractor models, specifies a plausible mechanism for feature binding and makes specific predictions about swap errors that are testable at behavioral and neurophysiological levels.

Neural evidence for categorical biases in location and orientation representations in a working memory task

Previous research demonstrated that visual representations in working memory exhibit biases with respect to the categorical structure of the stimulus space. However, a majority of those studies used behavioral measures of working memory, and it is not clear whether the working memory representations per se are influenced by the categorical structure or whether the biases arise in decision or response processes during the report. Here, I applied a multivariate decoding technique to EEG data collected during working memory tasks to determine whether neural activity associated with the representations in working memory is categorically biased prior to the report. I found that the decoding of spatial working memory was biased away from the nearest cardinal location, consistent with the biases observed in the behavioral responses. In a follow-up experiment which was designed to prevent the use of a response preparation strategy, I found that the decoding still exhibited categorical biases. Together, these results provide neural evidence that working memory representations themselves are categorically biased, imposing important constraints on the models of working memory representations.

Visual working memory items drift apart due to active, not passive, maintenance

How are humans capable of maintaining detailed representations of visual items in memory? When required to make fine discriminations, we sometimes implicitly differentiate memory representations away from each other to reduce interitem confusion. However, this separation of representations can inadvertently lead memories to be recalled as biased away from other memory items, a phenomenon termed repulsion bias. Using a nonretinotopically specific working memory paradigm, we found stronger repulsion bias with longer working memory delays, but only when items were actively maintained. These results suggest that (a) repulsion bias can reflect a mnemonic phenomenon, distinct from perceptually driven observations of repulsion bias; and (b) mnemonic repulsion bias is ongoing during maintenance and dependent on attention to internally maintained memory items. These results support theories of working memory where items are represented interdependently and further reveals contexts where stronger attention to working memory items during maintenance increases repulsion bias between them. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory

There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Embodied working memory during ongoing input streams

Sensory stimuli endow animals with the ability to generate an internal representation. This representation can be maintained for a certain duration in the absence of previously elicited inputs. The reliance on an internal representation rather than purely on the basis of external stimuli is a hallmark feature of higher-order functions such as working memory. Patterns of neural activity produced in response to sensory inputs can continue long after the disappearance of previous inputs. Experimental and theoretical studies have largely invested in understanding how animals faithfully maintain sensory representations during ongoing reverberations of neural activity. However, these studies have focused on preassigned protocols of stimulus presentation, leaving out by default the possibility of exploring how the content of working memory interacts with ongoing input streams. Here, we study working memory using a network of spiking neurons with dynamic synapses subject to short-term and long-term synaptic plasticity. The formal model is embodied in a physical robot as a companion approach under which neuronal activity is directly linked to motor output. The artificial agent is used as a methodological tool for studying the formation of working memory capacity. To this end, we devise a keyboard listening framework to delineate the context under which working memory content is (1) refined, (2) overwritten or (3) resisted by ongoing new input streams. Ultimately, this study takes a neurorobotic perspective to resurface the long-standing implication of working memory in flexible cognition.

Increased repulsion of working memory representations in schizophrenia

Computational neuroscience models propose that working memory (WM) involves recurrent excitatory feedback loops that maintain firing over time along with lateral inhibition that prevents the spreading of activity to other feature values. In behavioral paradigms, this lateral inhibition appears to cause a repulsion of WM representations away from each other and from other strong sources of input. Recent computational models of schizophrenia have proposed that reduction in the strength of inhibition relative to strength of excitation may underlie impaired cognition, and this leads to the prediction that repulsion effects should be reduced in people with schizophrenia spectrum disorders (PSZ) relative to healthy control subjects (HCS). We tested this hypothesis in 2 experiments measuring WM repulsion effects. In Experiment 1, 45 PSZ and 32 HCS remembered the location of a single object relative to a centrally presented visual landmark and reported this location after a short delay. The reported location was repelled away from the landmark in both groups, but this repulsion effect was increased rather than decreased in PSZ relative to HCS. In Experiment 2, 41 PSZ and 34 HCS remembered 2 sequentially presented orientations and reported each orientation after a short delay. The reported orientations were biased away from each other in both groups, and this repulsion effect was again more pronounced in PSZ than in HCS. Contrary to the widespread hypothesis of reduced inhibition in schizophrenia, we provide robust evidence from 2 experiments showing that the behavioral performance of PSZ exhibited an exaggeration rather than a reduction of competitive inhibition. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Stochastic sampling provides a unifying account of visual working memory limits

Research into human working memory limits has been shaped by the competition between different formal models, with a central point of contention being whether internal representations are continuous or discrete. Here we describe a sampling approach derived from principles of neural coding as a framework to understand working memory limits. Reconceptualizing existing models in these terms reveals strong commonalities between seemingly opposing accounts, but also allows us to identify specific points of difference. We show that the discrete versus continuous nature of sampling is not critical to model fits, but that, instead, random variability in sample counts is the key to reproducing human performance in both single- and whole-report tasks. A probabilistic limit on the number of items successfully retrieved is an emergent property of stochastic sampling, requiring no explicit mechanism to enforce it. These findings resolve discrepancies between previous accounts and establish a unified computational framework for working memory that is compatible with neural principles.

Working Memory Load Modulates Neuronal Coupling

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1-3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC-FEF-LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.

Exploring brain-behavior relationships in the N-back task

Working memory (WM) function has traditionally been investigated in terms of two dimensions: within-individual effects of WM load, and between-individual differences in task performance. In human neuroimaging studies, the N-back task has frequently been used to study both. A reliable finding is that activation in frontoparietal regions exhibits an inverted-U pattern, such that activity tends to decrease at high load levels. Yet it is not known whether such U-shaped patterns are a key individual differences factor that can predict load-related changes in task performance. The current study investigated this question by manipulating load levels across a much wider range than explored previously (N ​= ​1-6), and providing a more comprehensive examination of brain-behavior relationships. In a sample of healthy young adults (n ​= ​57), the analysis focused on a distinct region of left lateral prefrontal cortex (LPFC) identified in prior work to show a unique relationship with task performance and WM function. In this region it was the linear slope of load-related activity, rather than the U-shaped pattern, that was positively associated with individual differences in target accuracy. Comprehensive supplemental analyses revealed the brain-wide selectivity of this pattern. Target accuracy was also independently predicted by the global resting-state connectivity of this LPFC region. These effects were robust, as demonstrated by cross-validation analyses and out-of-sample prediction, and also critically, were primarily driven by the high-load conditions. Together, the results highlight the utility of high-load conditions for investigating individual differences in WM function.

Scene memory and spatial inhibition in visual search : A neural dynamic process model and new experimental evidence

Any object-oriented action requires that the object be first brought into the attentional foreground, often through visual search. Outside the laboratory, this would always take place in the presence of a scene representation acquired from ongoing visual exploration. The interaction of scene memory with visual search is still not completely understood. Feature integration theory (FIT) has shaped both research on visual search, emphasizing the scaling of search times with set size when searches entail feature conjunctions, and research on visual working memory through the change detection paradigm. Despite its neural motivation, there is no consistently neural process account of FIT in both its dimensions. We propose such an account that integrates (1) visual exploration and the building of scene memory, (2) the attentional detection of visual transients and the extraction of search cues, and (3) visual search itself. The model uses dynamic field theory in which networks of neural dynamic populations supporting stable activation states are coupled to generate sequences of processing steps. The neural architecture accounts for basic findings in visual search and proposes a concrete mechanism for the integration of working memory into the search process. In a behavioral experiment, we address the long-standing question of whether both the overall speed and the efficiency of visual search can be improved by scene memory. We find both effects and provide model fits of the behavioral results. In a second experiment, we show that the increase in efficiency is fragile, and trace that fragility to the resetting of spatial working memory.

Network Models Predict That Pyramidal Neuron Hyperexcitability and Synapse Loss in the dlPFC Lead to Age-Related Spatial Working Memory Impairment in Rhesus Monkeys

Behavioral studies have shown spatial working memory impairment with aging in several animal species, including humans. Persistent activity of layer 3 pyramidal dorsolateral prefrontal cortex (dlPFC) neurons during delay periods of working memory tasks is important for encoding memory of the stimulus. In vitro studies have shown that these neurons undergo significant age-related structural and functional changes, but the extent to which these changes affect neural mechanisms underlying spatial working memory is not understood fully. Here, we confirm previous studies showing impairment on the Delayed Recognition Span Task in the spatial condition (DRSTsp), and increased in vitro action potential firing rates (hyperexcitability), across the adult life span of the rhesus monkey. We use a bump attractor model to predict how empirically observed changes in the aging dlPFC affect performance on the Delayed Response Task (DRT), and introduce a model of memory retention in the DRSTsp. Persistent activity-and, in turn, cognitive performance-in both models was affected much more by hyperexcitability of pyramidal neurons than by a loss of synapses. Our DRT simulations predict that additional changes to the network, such as increased firing of inhibitory interneurons, are needed to account for lower firing rates during the DRT with aging reported in vivo. Synaptic facilitation was an essential feature of the DRSTsp model, but it did not compensate fully for the effects of the other age-related changes on DRT performance. Modeling pyramidal neuron hyperexcitability and synapse loss simultaneously led to a partial recovery of function in both tasks, with the simulated level of DRSTsp impairment similar to that observed in aging monkeys. This modeling work integrates empirical data across multiple scales, from synapse counts to cognitive testing, to further our understanding of aging in non-human primates.

A Robust Model of Gated Working Memory

Gated working memory is defined as the capacity of holding arbitrary information at any time in order to be used at a later time. Based on electrophysiological recordings, several computational models have tackled the problem using dedicated and explicit mechanisms. We propose instead to consider an implicit mechanism based on a random recurrent neural network. We introduce a robust yet simple reservoir model of gated working memory with instantaneous updates. The model is able to store an arbitrary real value at random time over an extended period of time. The dynamics of the model is a line attractor that learns to exploit reentry and a nonlinearity during the training phase using only a few representative values. A deeper study of the model shows that there is actually a large range of hyperparameters for which the results hold (e.g., number of neurons, sparsity, global weight scaling) such that any large enough population, mixing excitatory and inhibitory neurons, can quickly learn to realize such gated working memory. In a nutshell, with a minimal set of hypotheses, we show that we can have a robust model of working memory. This suggests this property could be an implicit property of any random population, that can be acquired through learning. Furthermore, considering working memory to be a physically open but functionally closed system, we give account on some counterintuitive electrophysiological recordings.

Oscillatory recurrent gated neural integrator circuits (ORGaNICs), a unifying theoretical framework for neural dynamics

Working memory is an example of a cognitive and neural process that is not static but evolves dynamically with changing sensory inputs; another example is motor preparation and execution. We introduce a theoretical framework for neural dynamics, based on oscillatory recurrent gated neural integrator circuits (ORGaNICs), and apply it to simulate key phenomena of working memory and motor control. The model circuits simulate neural activity with complex dynamics, including sequential activity and traveling waves of activity, that manipulate (as well as maintain) information during working memory. The same circuits convert spatial patterns of premotor activity to temporal profiles of motor control activity and manipulate (e.g., time warp) the dynamics. Derivative-like recurrent connectivity, in particular, serves to manipulate and update internal models, an essential feature of working memory and motor execution. In addition, these circuits incorporate recurrent normalization, to ensure stability over time and robustness with respect to perturbations of synaptic weights.

Hierarchical recruitment of competition alleviates working memory overload in a frontoparietal model

The storage limitations of visual working memory have been the subject of intense research interest for several decades, but few studies have systematically investigated the dependence of these limitations on memory load that exceeds our retention abilities. Under this real-world scenario, performance typically declines beyond a critical load among low-performing subjects, a phenomenon known as working memory overload. We used a frontoparietal cortical model to test the hypothesis that high-performing subjects select a manageable number of items for storage, thereby avoiding overload. The model accounts for behavioral and electrophysiological data from high-performing subjects in a parameter regime where competitive encoding in its prefrontal network selects items for storage, interareal projections sustain their representations after stimulus offset, and weak dynamics in its parietal network limit their mutual interference. Violation of these principles accounts for these data among low-performing subjects, implying that poor visual working memory performance reflects poor control over frontoparietal circuitry, making testable predictions for experiments.

Stability of working memory in continuous attractor networks under the control of short-term plasticity

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes; and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory.

The ability to transiently memorize positions in the visual field is crucial for behavior. Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states, that reflects the continuum of positions in the environment. However, the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate. Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections, thereby “pinning down” memorized positions. Here, we present a general theory that accurately predicts the extent of this “pinning down” by short-term plasticity in a broad class of biologically plausible network models, thereby untangling the interplay of varying biological sources of noise with short-term plasticity. Importantly, our work provides a novel theoretical link from the microscopic substrate of working memory—neurons and synaptic connections—to observable behavioral correlates, for example the susceptibility to distracting stimuli.

Chunking as a rational strategy for lossy data compression in visual working memory

The nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a "chunking" process to optimize performance on visual working memory tasks. We show that such chunking can: (a) facilitate performance improvements for abstract capacity-limited systems, (b) be optimized through reinforcement, (c) be implemented by center-surround dynamics, and (d) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, interitem dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for reevaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects. (PsycINFO Database Record

Independent working memory resources for egocentric and allocentric spatial information

Visuospatial working memory enables us to maintain access to visual information for processing even when a stimulus is no longer present, due to occlusion, our own movements, or transience of the stimulus. Here we show that, when localizing remembered stimuli, the precision of spatial recall does not rely solely on memory for individual stimuli, but additionally depends on the relative distances between stimuli and visual landmarks in the surroundings. Across three separate experiments, we consistently observed a spatially selective improvement in the precision of recall for items located near a persistent landmark. While the results did not require that the landmark be visible throughout the memory delay period, it was essential that it was visible both during encoding and response. We present a simple model that can accurately capture human performance by considering relative (allocentric) spatial information as an independent localization estimate which degrades with distance and is optimally integrated with egocentric spatial information. Critically, allocentric information was encoded without cost to egocentric estimation, demonstrating independent storage of the two sources of information. Finally, when egocentric and allocentric estimates were put in conflict, the model successfully predicted the resulting localization errors. We suggest that the relative distance between stimuli represents an additional, independent spatial cue for memory recall. This cue information is likely to be critical for spatial localization in natural settings which contain an abundance of visual landmarks.

A resource-rational theory of set size effects in human visual working memory

Encoding precision in visual working memory decreases with the number of encoded items. Here, we propose a normative theory for such set size effects: the brain minimizes a weighted sum of an error-based behavioral cost and a neural encoding cost. We construct a model from this theory and find that it predicts set size effects. Notably, these effects are mediated by probing probability, which aligns with previous empirical findings. The model accounts well for effects of both set size and probing probability on encoding precision in nine delayed-estimation experiments. Moreover, we find support for the prediction that the total amount of invested resource can vary non-monotonically with set size. Finally, we show that it is sometimes optimal to encode only a subset or even none of the relevant items in a task. Our findings raise the possibility that cognitive "limitations" arise from rational cost minimization rather than from constraints.

Efficient Coding in Visual Working Memory Accounts for Stimulus-Specific Variations in Recall

Recall of visual features from working memory varies in both bias and precision depending on stimulus parameters. Whereas a number of models can approximate the average distribution of recall error across target stimuli, attempts to model how error varies with the choice of target have been ad hoc. Here we adapt a neural model of working memory to provide a principled account of these stimulus-specific effects, by allowing each neuron's tuning function to vary according to the principle of efficient coding, which states that neural responses should be optimized with respect to the frequency of stimuli in nature. For orientation, this means incorporating a prior that favors cardinal over oblique orientations. While continuing to capture the changes in error distribution with set size, the resulting model accurately described stimulus-specific variations as well, better than a slot-based competitor. Efficient coding produces a repulsive bias away from cardinal orientations, a bias that ought to be sensitive to changes in the environmental statistics. We subsequently tested whether shifts in the stimulus distribution influenced response bias to uniformly sampled target orientations in human subjects (of either sex). Across adaptation blocks, we manipulated the distribution of nontarget items by sampling from a bimodal congruent (incongruent) distribution with peaks centered on cardinal (oblique) orientations. Preadaptation responses were repulsed away from the cardinal axes. However, exposure to the incongruent distribution produced systematic decreases in repulsion that persisted after adaptation. This result confirms the role of prior expectation in generating stimulus-specific effects and validates the neural framework.

SIGNIFICANCE STATEMENT Theories of neural coding have been used successfully to explain how errors in recall from working memory depend on the number of items stored. However, recall of visual features also shows stimulus-specific variation in bias and precision. Here we unify two previously unconnected theories, the neural resource model of working memory and the efficient coding framework, to provide a principled account of these stimulus-specific effects. Given the importance of working memory limitations to multiple aspects of human and animal behavior, and the recent high-profile advances in theories of efficient coding, our modeling framework provides a richer, yet parsimonious, description of how orientation encoding influences visual working memory performance.

Fractionating the Neurocognitive Mechanisms Underlying Working Memory: Independent Effects of Dopamine and Parkinson's Disease

Deficits in working memory (WM) in Parkinson’s disease (PD) are often considered to be secondary to dopaminergic depletion. However, the neurocognitive mechanisms by which dopamine causes these deficits remain highly contested, and PD is now also known to be associated with nondopaminergic pathology. Here, we examined how PD and dopaminergic medication modulate three components of WM: maintenance over time, updating contents with new information and making memories distracter-resistant. Compared with controls, patients were disproportionately impaired when retaining information for longer durations. By applying a probabilistic model, we were able to reveal that the source of this error was selectively due to precision of memory representations degrading over time. By contrast, replenishing dopamine levels in PD improved executive control over both the ability to ignore and update, but did not affect maintenance of information across time. This was due to a decrease in guess responses, consistent with the view that dopamine serves to prevent WM representations being corrupted by irrelevant information, but has no impact on information decay. Cumulatively, these results reveal a dissociation in the neural mechanisms underlying poor WM: whereas dopamine reduces interference, nondopaminergic systems in PD appear to modulate processes that prevent information decaying more quickly over time.

Slot-like capacity and resource-like coding in a neural model of multiple-item working memory

For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of "slots," each with a fixed resolution. On the other hand, any number of items may be stored but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data or speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity, and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioral data from human and nonhuman primate studies, and they demonstrate that feedback inhibition lowers both capacity and coding fidelity. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks. NEW & NOTEWORTHY Working memory is the ability to keep information in mind and is fundamental to cognition. It is actively debated whether the storage limitations of working memory reflect a small number of storage units (slots) or a decrease in coding resolution as a limited resource is allocated to more items. In a cortical model, we found that slot-like capacity and resource-like neural coding resulted from the same mechanism, offering an integrated explanation for storage limitations.

Synaptic mechanisms of interference in working memory

Information from preceding trials of cognitive tasks can bias performance in the current trial, a phenomenon referred to as interference. Subjects performing visual working memory tasks exhibit interference in their responses: the recalled target location is biased in the direction of the target presented on the previous trial. We present modeling work that develops a probabilistic inference model of this history-dependent bias, and links our probabilistic model to computations of a recurrent network wherein short-term facilitation accounts for the observed bias. Network connectivity is reshaped dynamically during each trial, generating predictions from prior trial observations. Applying timescale separation methods, we obtain a low-dimensional description of the trial-to-trial bias based on the history of target locations. Furthermore, we demonstrate task protocols for which our model with facilitation performs better than a model with static connectivity: repetitively presented targets are better retained in working memory than targets drawn from uncorrelated sequences.

Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time

Short-term memories are thought to be maintained in the form of sustained spiking activity in neural populations. Decreases in recall precision observed with increasing number of memorized items can be accounted for by a limit on total spiking activity, resulting in fewer spikes contributing to the representation of each individual item. Longer retention intervals likewise reduce recall precision, but it is unknown what changes in population activity produce this effect. One possibility is that spiking activity becomes attenuated over time, such that the same mechanism accounts for both effects of set size and retention duration. Alternatively, reduced performance may be caused by drift in the encoded value over time, without a decrease in overall spiking activity. Human participants of either sex performed a variable-delay cued recall task with a saccadic response, providing a precise measure of recall latency. Based on a spike integration model of decision making, if the effects of set size and retention duration are both caused by decreased spiking activity, we would predict a fixed relationship between recall precision and response latency across conditions. In contrast, the drift hypothesis predicts no systematic changes in latency with increasing delays. Our results show both an increase in latency with set size, and a decrease in response precision with longer delays within each set size, but no systematic increase in latency for increasing delay durations. These results were quantitatively reproduced by a model based on a limited neural resource in which working memories drift rather than decay with time.

SIGNIFICANCE STATEMENT Rapid deterioration over seconds is a defining feature of short-term memory, but what mechanism drives this degradation of internal representations? Here, we extend a successful population coding model of working memory by introducing possible mechanisms of delay effects. We show that a decay in neural signal over time predicts that the time required for memory retrieval will increase with delay, whereas a random drift in the stored value predicts no effect of delay on retrieval time. Testing these predictions in a multi-item memory task with an eye movement response, we identified drift as a key mechanism of memory decline. These results provide evidence for a dynamic spiking basis for working memory, in contrast to recent proposals of activity-silent storage.

Spatiotemporal encoding of search strategies by prefrontal neurons

Working memory is capacity-limited. In everyday life we rarely notice this limitation, in part because we develop behavioral strategies that help mitigate the capacity limitation. How behavioral strategies are mediated at the neural level is unclear, but a likely locus is lateral prefrontal cortex (LPFC). Neurons in LPFC play a prominent role in working memory and have been shown to encode behavioral strategies. To examine the role of LPFC in overcoming working-memory limitations, we recorded the activity of LPFC neurons in animals trained to perform a serial self-ordered search task. This task measured the ability to prospectively plan the selection of unchosen spatial search targets while retrospectively tracking which targets were previously visited. We found that individual LPFC neurons encoded the spatial location of the current search target but also encoded the spatial location of targets up to several steps away in the search sequence. Neurons were more likely to encode prospective than retrospective targets. When subjects used a behavioral strategy of stereotyped target selection, mitigating the working-memory requirements of the task, not only did the number of selection errors decrease but there was a significant reduction in the strength of spatial encoding in LFPC. These results show that LPFC neurons have spatiotemporal mnemonic fields, in that their firing rates are modulated both by the spatial location of future selection behaviors and the temporal organization of that behavior. Furthermore, the strength of this tuning can be dynamically modulated by the demands of the task.

Synaptic efficacy shapes resource limitations in working memory

Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space. Our analysis relates the neural architecture of the network to accumulated errors and capacity limitations arising during the delay period of a multi-item WM task. Networks with stronger synapses support wider bumps that interact more, whereas networks with weaker synapses support narrower bumps that are more susceptible to noise perturbations. There is an optimal synaptic strength that both limits bump interaction events and the effects of noise perturbations. This optimum shifts to weaker synapses as the number of items stored in the network is increased. Our model not only provides a circuit-based explanation for WM capacity, but also speaks to how capacity relates to the arrangement of stored items in a feature space.

Evidence of gradual loss of precision for simple features and complex objects in visual working memory

Previous studies have suggested that people can maintain prioritized items in visual working memory for many seconds, with negligible loss of information over time. Such findings imply that working memory representations are robust to the potential contaminating effects of internal noise. However, once visual information is encoded into working memory, one might expect it to inevitably begin degrading over time, as this actively maintained information is no longer tethered to the original perceptual input. Here, we examined this issue by evaluating working memory for single central presentations of an oriented grating, color patch, or face stimulus, across a range of delay periods (1, 3, 6, or 12 s). We applied a mixture-model analysis to distinguish changes in memory precision over time from changes in the frequency of outlier responses that resemble random guesses. For all 3 types of stimuli, participants exhibited a clear and consistent decline in the precision of working memory as a function of temporal delay, as well as a modest increase in guessing-related responses for colored patches and face stimuli. We observed a similar loss of precision over time while controlling for temporal distinctiveness. Our results demonstrate that visual working memory is far from lossless: while basic visual features and complex objects can be maintained in a quite stable manner over time, these representations are still subject to noise accumulation and complete termination. (PsycINFO Database Record

The Effects of Similarity on High-Level Visual Working Memory Processing

Similarity has been observed to have opposite effects on visual working memory (VWM) for complex images. How can these discrepant results be reconciled? To answer this question, we used a change-detection paradigm to test visual working memory performance for multiple real-world objects. We found that working memory for moderate similarity items was worse than that for either high or low similarity items. This pattern was unaffected by manipulations of stimulus type (faces vs. scenes), encoding duration (limited vs. self-paced), and presentation format (simultaneous vs. sequential). We also found that the similarity effects differed in strength in different categories (scenes vs. faces). These results suggest that complex real-world objects are represented using a centre-surround inhibition organization. These results support the category-specific cortical resource theory and further suggest that centre-surround inhibition organization may differ by category.