Time-invariant working memory representations in the presence of code-morphing in the lateral prefrontal cortex

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

Mixed recurrent connectivity in primate prefrontal cortex

The functional properties of a network depend on its connectivity, which includes the strength of its inputs and the strength of the connections between its units, or recurrent connectivity. Because we lack a detailed description of the recurrent connectivity in the lateral prefrontal cortex of primates, we developed an indirect method to estimate it. This method leverages the elevated noise correlation of mutually-connected units. To estimate the connectivity of prefrontal regions, we trained recurrent neural network models with varying percentages of bump attractor connectivity and noise levels to match the noise correlation properties observed in two specific prefrontal regions: the dorsolateral prefrontal cortex and the frontal eye field. We found that models initialized with approximately 20% and 7.5% bump attractor connectivity closely matched the noise correlation properties of the frontal eye field and dorsolateral prefrontal cortex, respectively. These findings suggest that the different percentages of bump attractor connectivity may reflect distinct functional roles of these brain regions. Specifically, lower percentages of bump attractor units, associated with higher-dimensional representations, likely support more abstract neural representations in more anterior regions.

Compositional architecture: Orthogonal neural codes for task context and spatial memory in prefrontal cortex

The prefrontal cortex (PFC) is crucial for maintaining working memory across diverse cognitive tasks, yet how it adapts to varying task demands remains unclear. Compositional theories propose that cognitive processes in neural network rely on shared components that can be reused to support different behaviors. However, previous studies have suggested that working memory components are task specific, challenging this framework. Here, we revisit this question using a population-based approach. We recorded neural activity in macaque monkeys performing two spatial working memory tasks with opposing goals: one requiring movement toward previously presented spatial locations (look task) and the other requiring avoidance of those locations (no-look task). Despite differences in task demands, we found that spatial memory representations were largely conserved at the population level, with a common low-dimensional neural subspace encoding memory across both tasks. In parallel, task identity was encoded in an orthogonal subspace, providing a stable and independent representation of contextual information. These results provide neural evidence for a compositional model of working memory, where representational geometry enables the efficient and flexible reuse of mnemonic codes across behavioral contexts while maintaining an independent representation of context.

Robustness of working memory to prefrontal cortex microstimulation

Delay period activity in the dorso-lateral prefrontal cortex (dlPFC) has been linked to the maintenance and control of sensory information in working memory. The stability of working memory related signals found in such delay period activity is believed to support robust memory-guided behavior during sensory perturbations, such as distractors. Here, we directly probed dlPFC's delay period activity with a diverse set of activity perturbations, and measured their consequences on neural activity and behavior. We applied patterned microstimulation to the dlPFC of monkeys implanted with multi-electrode arrays by electrically stimulating different electrodes in the array while the monkeys performed a memory-guided saccade task. We found that the microstimulation perturbations affected spatial working memory-related signals in individual dlPFC neurons. However, task performance remained largely unaffected. These apparently contradictory observations could be understood by examining different dimensions of the dlPFC population activity. In dimensions where working memory related signals naturally evolved over time, microstimulation impacted neural activity. In contrast, in dimensions containing working memory related signals that were stable over time, microstimulation minimally impacted neural activity. This dissociation explained how working memory-related information could be stably maintained in dlPFC despite the activity changes induced by microstimulation. Thus, working memory processes are robust to a variety of activity perturbations in the dlPFC.

Diverse neuronal activity patterns contribute to the control of distraction in the prefrontal and parietal cortex

Goal-directed behavior requires the effective suppression of distractions to focus on the task at hand. Although experimental evidence suggests that brain areas in the prefrontal and parietal lobe contribute to the selection of task-relevant and the suppression of task-irrelevant stimuli, how conspicuous distractors are encoded and effectively ignored remains poorly understood. We recorded neuronal responses from 2 regions in the prefrontal and parietal cortex of macaques, the frontal eye field (FEF) and the lateral intraparietal (LIP) area, during a visual search task, in the presence and absence of a salient distractor. We found that in both areas, salient distractors are encoded by both response enhancement and suppression by distinct neuronal populations. In FEF, a larger proportion of units displayed suppression of responses to the salient distractor compared to LIP, with suppression effects in FEF being correlated with search time. Moreover, in FEF but not in LIP, the suppression for the salient distractor compared to non-salient distractors that shared the target color could not be accounted for by an enhancement of target features. These results reveal a distinct contribution of FEF in the suppression of salient distractors. Critically, we found that in both areas, the population level representations of the target and singleton locations were not orthogonal, suggesting a mechanism of interference from salient stimuli.

A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection different states in neural trajectories. The result shows that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilizes in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

Mixed selectivity: Cellular computations for complexity

The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.

The computational foundations of dynamic coding in working memory

Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.

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.

Top-down generation of low-resolution representations improves visual perception and imagination

Perception or imagination requires top-down signals from high-level cortex to primary visual cortex (V1) to reconstruct or simulate the representations bottom-up stimulated by the seen images. Interestingly, top-down signals in V1 have lower spatial resolution than bottom-up representations. It is unclear why the brain uses low-resolution signals to reconstruct or simulate high-resolution representations. By modeling the top-down pathway of the visual system using the decoder of a variational auto-encoder (VAE), we reveal that low-resolution top-down signals can better reconstruct or simulate the information contained in the sparse activities of V1 simple cells, which facilitates perception and imagination. This advantage of low-resolution generation is related to facilitating high-level cortex to form geometry-respecting representations observed in experiments. Furthermore, we present two findings regarding this phenomenon in the context of AI-generated sketches, a style of drawings made of lines. First, we found that the quality of the generated sketches critically depends on the thickness of the lines in the sketches: thin-line sketches are harder to generate than thick-line sketches. Second, we propose a technique to generate high-quality thin-line sketches: instead of directly using original thin-line sketches, we use blurred sketches to train VAE or GAN (generative adversarial network), and then infer the thin-line sketches from the VAE- or GAN-generated blurred sketches. Collectively, our work suggests that low-resolution top-down generation is a strategy the brain uses to improve visual perception and imagination, which inspires new sketch-generation AI techniques.

Sequential neuronal processing of number values, abstract decision, and action in the primate prefrontal cortex

Decision-making requires processing of sensory information, comparing the gathered evidence to make a judgment, and performing the action to communicate it. How neuronal representations transform during this cascade of representations remains a matter of debate. Here, we studied the succession of neuronal representations in the primate prefrontal cortex (PFC). We trained monkeys to judge whether a pair of sequentially presented displays had the same number of items. We used a combination of single neuron and population-level analyses and discovered a sequential transformation of represented information with trial progression. While numerical values were initially represented with high precision and in conjunction with detailed information such as order, the decision was encoded in a low-dimensional subspace of neural activity. This decision encoding was invariant to both retrospective numerical values and prospective motor plans, representing only the binary judgment of "same number" versus "different number," thus facilitating the generalization of decisions to novel number pairs. We conclude that this transformation of neuronal codes within the prefrontal cortex supports cognitive flexibility and generalizability of decisions to new conditions.

The neuronal implementation of representational geometry in primate prefrontal cortex

Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Each component was contributed by small subpopulations of neurons with distinct spiking properties and response dynamics. Modeling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.

Decoding working memory information from neurons with and without persistent activity in the primate prefrontal cortex

Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have challenged this idea. Theories that depend on the dynamic representation of information posit that stimulus information may be maintained by the activity pattern of neurons whose firing rate is not significantly elevated above their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Averaged across all neurons and stimuli, the firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory; however, this grand average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of stimulus information. These results reveal that prefrontal neurons that generate persistent activity maintain information more reliably during working memory.NEW & NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. Although the two models have been debated on theoretical terms, direct comparison of empirical results has been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.

Optimal information loading into working memory explains dynamic coding in the prefrontal cortex

Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.

Common Mechanisms of Learning in Motor and Cognitive Systems

Rapid progress in our understanding of the brain's learning mechanisms has been accomplished over the past decade, particularly with conceptual advances, including representing behavior as a dynamical system, large-scale neural population recordings, and new methods of analysis of neuronal populations. However, motor and cognitive systems have been traditionally studied with different methods and paradigms. Recently, some common principles, evident in both behavior and neural activity, that underlie these different types of learning have become to emerge. Here we review results from motor and cognitive learning, relying on different techniques and studying different systems to understand the mechanisms of learning. Movement is intertwined with cognitive operations, and its dynamics reflect cognitive variables. Training, in either motor or cognitive tasks, involves recruitment of previously unresponsive neurons and reorganization of neural activity in a low dimensional manifold. Mapping of new variables in neural activity can be very rapid, instantiating flexible learning of new tasks. Communication between areas is just as critical a part of learning as are patterns of activity within an area emerging with learning. Common principles across systems provide a map for future research.

Distinct Lateral Prefrontal Regions Are Organized in an Anterior-Posterior Functional Gradient

The dorsolateral prefrontal cortex (dlPFC) is composed of multiple anatomically defined regions involved in higher-order cognitive processes, including working memory and selective attention. It is organized in an anterior-posterior global gradient where posterior regions track changes in the environment, whereas anterior regions support abstract neural representations. However, it remains unknown if such a global gradient results from a smooth gradient that spans regions or an emergent property arising from functionally distinct regions, that is, an areal gradient. Here, we recorded single neurons in the dlPFC of nonhuman primates trained to perform a memory-guided saccade task with an interfering distractor and analyzed their physiological properties along the anterior-posterior axis. We found that these physiological properties were best described by an areal gradient. Further, population analyses revealed that there is a distributed representation of spatial information across the dlPFC. Our results validate the functional boundaries between anatomically defined dlPFC regions and highlight the distributed nature of computations underlying working memory across the dlPFC.SIGNIFICANCE STATEMENT Activity of frontal lobe regions is known to possess an anterior-posterior functional gradient. However, it is not known whether this gradient is the result of individual brain regions organized in a gradient (like a staircase), or a smooth gradient that spans regions (like a slide). Analysis of physiological properties of individual neurons in the primate frontal regions suggest that individual regions are organized as a gradient, rather than a smooth gradient. At the population level, working memory was more prominent in posterior regions, although it was also present in anterior regions. This is consistent with the functional segregation of brain regions that is also observed in other systems (i.e., the visual system).

Comparing representations and computations in single neurons versus neural networks

Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.

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.

The role of conjunctive representations in prioritizing and selecting planned actions

For flexible goal-directed behavior, prioritizing and selecting a specific action among multiple candidates are often important. Working memory has long been assumed to play a role in prioritization and planning, while bridging cross-temporal contingencies during action selection. However, studies of working memory have mostly focused on memory for single components of an action plan, such as a rule or a stimulus, rather than management of all of these elements during planning. Therefore, it is not known how post-encoding prioritization and selection operate on the entire profile of representations for prospective actions. Here, we assessed how such control processes unfold over action representations, highlighting the role of conjunctive representations that nonlinearly integrate task-relevant features during maintenance and prioritization of action plans. For each trial, participants prepared two independent rule-based actions simultaneously, then they were retro-cued to select one as their response. Prior to the start of the trial, one rule-based action was randomly assigned to be high priority by cueing that it was more likely to be tested. We found that both full action plans were maintained as conjunctive representations during action preparation, regardless of priority. However, during output selection, the conjunctive representation of the high-priority action plan was more enhanced and readily selected as an output. Furthermore, the strength of the high-priority conjunctive representation was associated with behavioral interference when the low-priority action was tested. Thus, multiple alternate upcoming actions were maintained as integrated representations and served as the target of post-encoding attentional selection mechanisms to prioritize and select an action from within working memory.

Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex

A large body of recent work suggests that neural representations in prefrontal cortex (PFC) are changing over time to adapt to task demands. However, it remains unclear whether and how such dynamic coding schemes depend on the encoded variable and are influenced by anatomical constraints. Using a cued attention task and multivariate classification methods, we show that neuronal ensembles in PFC encode and retain in working memory spatial and color attentional instructions in an anatomically specific manner. Spatial instructions could be decoded both from the frontal eye field (FEF) and the ventrolateral PFC (vlPFC) population, albeit more robustly from FEF, whereas color instructions were decoded more robustly from vlPFC. Decoding spatial and color information from vlPFC activity in the high-dimensional state space indicated stronger dynamics for color, across the cue presentation and memory periods. The change in the color code was largely due to rapid changes in the network state during the transition to the delay period. However, we found that dynamic vlPFC activity contained time-invariant color information within a low-dimensional subspace of neural activity that allowed for stable decoding of color across time. Furthermore, spatial attention influenced decoding of stimuli features profoundly in vlPFC, but less so in visual area V4. Overall, our results suggest that dynamic population coding of attentional instructions within PFC is shaped by anatomical constraints and can coexist with stable subspace coding that allows time-invariant decoding of information about the future target.

Geometry of neural computation unifies working memory and planning

Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. In task-optimized recurrent neural networks, we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from the prefrontal cortex during working memory tasks. Our experiments revealed that human behavior is consistent with contingency representations and not with traditional sensory models of working memory. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision-making.

A Nonlinear Hidden Layer Enables Actor-Critic Agents to Learn Multiple Paired Association Navigation

Navigation to multiple cued reward locations has been increasingly used to study rodent learning. Though deep reinforcement learning agents have been shown to be able to learn the task, they are not biologically plausible. Biologically plausible classic actor-critic agents have been shown to learn to navigate to single reward locations, but which biologically plausible agents are able to learn multiple cue-reward location tasks has remained unclear. In this computational study, we show versions of classic agents that learn to navigate to a single reward location, and adapt to reward location displacement, but are not able to learn multiple paired association navigation. The limitation is overcome by an agent in which place cell and cue information are first processed by a feedforward nonlinear hidden layer with synapses to the actor and critic subject to temporal difference error-modulated plasticity. Faster learning is obtained when the feedforward layer is replaced by a recurrent reservoir network.

A Stable Population Code for Attention in Prefrontal Cortex Leads a Dynamic Attention Code in Visual Cortex

Attention often requires maintaining a stable mental state over time while simultaneously improving perceptual sensitivity. These requirements place conflicting demands on neural populations, as sensitivity implies a robust response to perturbation by incoming stimuli, which is antithetical to stability. Functional specialization of cortical areas provides one potential mechanism to resolve this conflict. We reasoned that attention signals in executive control areas might be highly stable over time, reflecting maintenance of the cognitive state, thereby freeing up sensory areas to be more sensitive to sensory input (i.e., unstable), which would be reflected by more dynamic attention signals in those areas. To test these predictions, we simultaneously recorded neural populations in prefrontal cortex (PFC) and visual cortical area V4 in rhesus macaque monkeys performing an endogenous spatial selective attention task. Using a decoding approach, we found that the neural code for attention states in PFC was substantially more stable over time compared with the attention code in V4 on a moment-by-moment basis, in line with our guiding thesis. Moreover, attention signals in PFC predicted the future attention state of V4 better than vice versa, consistent with a top-down role for PFC in attention. These results suggest a functional specialization of attention mechanisms across cortical areas with a division of labor. PFC signals the cognitive state and maintains this state stably over time, whereas V4 responds to sensory input in a manner dynamically modulated by that cognitive state.SIGNIFICANCE STATEMENT Attention requires maintaining a stable mental state while simultaneously improving perceptual sensitivity. We hypothesized that these two demands (stability and sensitivity) are distributed between prefrontal and visual cortical areas, respectively. Specifically, we predicted attention signals in visual cortex would be less stable than in prefrontal cortex, and furthermore prefrontal cortical signals would predict attention signals in visual cortex in line with the hypothesized role of prefrontal cortex in top-down executive control. Our results are consistent with suggestions deriving from previous work using separate recordings in the two brain areas in different animals performing different tasks and represent the first direct evidence in support of this hypothesis with simultaneous multiarea recordings within individual animals.

The orbitofrontal cortex maps future navigational goals

Accurate navigation to a desired goal requires consecutive estimates of spatial relationships between the current position and future destination throughout the journey. Although neurons in the hippocampal formation can represent the position of an animal as well as its nearby trajectories^1-7, their role in determining the destination of the animal has been questioned^8,9. It is, thus, unclear whether the brain can possess a precise estimate of target location during active environmental exploration. Here we describe neurons in the rat orbitofrontal cortex (OFC) that form spatial representations persistently pointing to the subsequent goal destination of an animal throughout navigation. This destination coding emerges before the onset of navigation, without direct sensory access to a distal goal, and even predicts the incorrect destination of an animal at the beginning of an error trial. Goal representations in the OFC are maintained by destination-specific neural ensemble dynamics, and their brief perturbation at the onset of a journey led to a navigational error. These findings suggest that the OFC is part of the internal goal map of the brain, enabling animals to navigate precisely to a chosen destination that is beyond the range of sensory perception.

Working Memory: From Neural Activity to the Sentient Mind

Working memory (WM) is the ability to maintain and manipulate information in the conscious mind over a timescale of seconds. This ability is thought to be maintained through the persistent discharges of neurons in a network of brain areas centered on the prefrontal cortex, as evidenced by neurophysiological recordings in nonhuman primates, though both the localization and the neural basis of WM has been a matter of debate in recent years. Neural correlates of WM are evident in species other than primates, including rodents and corvids. A specialized network of excitatory and inhibitory neurons, aided by neuromodulatory influences of dopamine, is critical for the maintenance of neuronal activity. Limitations in WM capacity and duration, as well as its enhancement during development, can be attributed to properties of neural activity and circuits. Changes in these factors can be observed through training-induced improvements and in pathological impairments. WM thus provides a prototypical cognitive function whose properties can be tied to the spiking activity of brain neurons. © 2021 American Physiological Society. Compr Physiol 11:1-41, 2021.

A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

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.

Persistent Activity During Working Memory From Front to Back

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

The dimensionality of neural representations for control

Cognitive control allows us to think and behave flexibly based on our context and goals. At the heart of theories of cognitive control is a control representation that enables the same input to produce different outputs contingent on contextual factors. In this review, we focus on an important property of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. We will discuss the implications of this trade-off for cognitive control. We will then briefly review current neuroscience findings regarding the dimensionality of control representations in the brain, particularly the prefrontal cortex. We conclude by highlighting open questions and crucial directions for future research.

Heterogeneous value coding in orbitofrontal populations

Value signals in the brain are important for learning, decision-making, and orienting behavior toward relevant goals. Although they can play different roles in behavior and cognition, value representations are often considered to be uniform and static signals. Nonetheless, contextual and mixed representations of value have been widely reported. Here, we review the evidence for heterogeneity in value coding and dynamics in the orbitofrontal cortex. We argue that this diversity plays a key role in the representation of value itself and allows neurons to integrate value with other behaviorally relevant information. We also discuss modeling approaches that can dissociate potential functions of heterogeneous value codes and provide further insight into its importance in behavior and cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Statistical methods for dissecting interactions between brain areas

The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.

[Review of cognitive enhancement techniques based on the combination of cognitive training and transcranial direct current stimulation]

Cognitive enhancement refers to the technology of enhancing or expanding the cognitive and emotional abilities of people without psychosis based on relevant knowledge of neurobiology. The common methods of cognitive enhancement include transcranial direct current stimulation (tDCS) and cognitive training (CT). tDCS takes effect quickly, with a short effective time, while CT takes longer to work, requiring several weeks of training, with a longer effective time. In recent years, some researchers have begun to use the method of tDCS combined with CT to regulate the cognitive function. This paper will sort out and summarize this topic from five aspects: perception, attention, working memory, decision-making and other cognitive abilities. Finally, the application prospect and challenges of technology are prospected.

Minimally dependent activity subspaces for working memory and motor preparation in the lateral prefrontal cortex

The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here, we show that the lateral prefrontal cortex of non-human primates contains two minimally dependent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results provide new insights into neural processing in prefrontal regions.

Plasticity of Persistent Activity and Its Constraints

Stimulus information is maintained in working memory by action potentials that persist after the stimulus is no longer physically present. The prefrontal cortex is a critical brain area that maintains such persistent activity due to an intrinsic network with unique synaptic connectivity, NMDA receptors, and interneuron types. Persistent activity can be highly plastic depending on task demands but it also appears in naïve subjects, not trained or required to perform a task at all. Here, we review what aspects of persistent activity remain constant and what factors can modify it, focusing primarily on neurophysiological results from non-human primate studies. Changes in persistent activity are constrained by anatomical location, with more ventral and more anterior prefrontal areas exhibiting the greatest capacity for plasticity, as opposed to posterior and dorsal areas, which change relatively little with training. Learning to perform a cognitive task for the first time, further practicing the task, and switching between learned tasks can modify persistent activity. The ability of the prefrontal cortex to generate persistent activity also depends on age, with changes noted between adolescence, adulthood, and old age. Mean firing rates, variability and correlation of persistent discharges, but also time-varying firing rate dynamics are altered by these factors. Plastic changes in the strength of intrinsic network connections can be revealed by the analysis of synchronous spiking between neurons. These results are essential for understanding how the prefrontal cortex mediates working memory and intelligent behavior.