Learning attentional templates for value-based decision-making.

Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

Building compositional tasks with shared neural subspaces.

Cognition is remarkably flexible; we are able to rapidly learn and perform many different tasks1. Theoretical modeling has shown artificial neural networks trained to perform multiple tasks will re-use representations2 and computational components3 across tasks. By composing tasks from these sub-components, an agent can flexibly switch between tasks and rapidly learn new tasks4. Yet, whether such compositionality is found in the brain is unknown. Here, we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task compositionally combining these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. Neural recordings found task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Subspaces were flexibly engaged as monkeys discovered the task in effect; their internal belief about the current task predicted the strength of representations in task-relevant subspaces. In sum, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations across tasks.

Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype.

Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.

Corvids optimize working memory by categorizing continuous stimuli.

Working memory (WM) is a crucial element of the higher cognition of primates and corvid songbirds. Despite its importance, WM has a severely limited capacity and is vulnerable to noise. In primates, attractor dynamics mitigate the effect of noise by discretizing continuous information. Yet, it remains unclear whether similar dynamics are seen in avian brains. Here, we show jackdaws (Corvus monedula) have similar behavioral biases as humans; memories are less precise and more biased as memory demands increase. Model-based analysis reveal discrete attractors are evenly spread across the stimulus space. Altogether, our comparative approach suggests attractor dynamics in primates and corvids mitigate the effect of noise by systematically drifting towards specific attractors. By demonstrating this effect in an evolutionary distant species, our results strengthen attractor dynamics as general, adaptive biological principle to efficiently use WM.

The neural basis of swap errors in working memory.

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once - such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms through trial-by-trial analysis of neural population recordings from posterior and frontal brain regions while monkeys perform two multi-stimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location (requiring selection from working memory) or will be shown at the corresponding location (requiring attention to a position). Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected incorrectly from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results suggest an alternative to established views on the neural origins of swap errors, and highlight selection from and manipulation in working memory as crucial - yet surprisingly brittle - neural processes.

Working memory control dynamics follow principles of spatial computing.

Working memory (WM) allows us to remember and selectively control a limited set of items. Neural evidence suggests it is achieved by interactions between bursts of beta and gamma oscillations. However, it is not clear how oscillations, reflecting coherent activity of millions of neurons, can selectively control individual WM items. Here we propose the novel concept of spatial computing where beta and gamma interactions cause item-specific activity to flow spatially across the network during a task. This way, control-related information such as item order is stored in the spatial activity independent of the detailed recurrent connectivity supporting the item-specific activity itself. The spatial flow is in turn reflected in low-dimensional activity shared by many neurons. We verify these predictions by analyzing local field potentials and neuronal spiking. We hypothesize that spatial computing can facilitate generalization and zero-shot learning by utilizing spatial component as an additional information encoding dimension.

Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks.

Cognition is flexible. Behaviors can change on a moment-by-moment basis. Such flexibility is thought to rely on the brain's ability to route information through different networks of brain regions in order to support different cognitive computations. However, the mechanisms that determine which network of brain regions is engaged are unknown. To address this, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice. Different dimensions within the population activity of each brain region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed, allowing a brain region to simultaneously interact with multiple independent, yet overlapping, networks. Alignment of neural activity within a region to a specific subspace network dimension predicted how neural activity propagated between regions. Thus, changing the geometry of the neural representation within a brain region could be a mechanism to selectively engage different brain-wide networks to support cognitive flexibility.

Fast rule switching and slow rule updating in a perceptual categorization task.

To adapt to a changing world, we must be able to switch between rules already learned and, at other times, learn rules anew. Often we must do both at the same time, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior, we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions.

Perineuronal Nets in the Dorsomedial Striatum Contribute to Behavioral Dysfunction in Mouse Models of Excessive Repetitive Behavior.

Background: Excessive repetitive behavior is a debilitating symptom of several neuropsychiatric disorders. Parvalbumin-positive inhibitory interneurons in the dorsal striatum have been linked to repetitive behavior, and a sizable portion of these cells are surrounded by perineuronal nets (PNNs), specialized extracellular matrix structures. Although PNNs have been associated with plasticity and neuropsychiatric disease, no previous studies have investigated their involvement in excessive repetitive behavior. Methods: We used histochemistry and confocal imaging to investigate PNNs surrounding parvalbumin-positive cells in the dorsal striatum of 4 mouse models of excessive repetitive behavior (BTBR, Cntnap2, Shank3, prenatal valproate treatment). We then investigated one of these models, the BTBR mouse, in detail, with DiI labeling, in vivo and in vitro recordings, and behavioral analyses. We next degraded PNNs in the dorsomedial striatum (DMS) using the enzyme chondroitinase ABC and assessed dendritic spine density, electrophysiology, and repetitive behavior. Results: We found a greater percentage of parvalbumin-positive interneurons with PNNs in the DMS of all 4 mouse models of excessive repetitive behavior compared with control mice. In BTBR mice, we found fewer dendritic spines on medium spiny neurons (targets of parvalbumin-positive interneurons) and differences in neuronal oscillations as well as inhibitory postsynaptic potentials compared with control mice. Reduction of DMS PNNs in BTBR mice altered dendritic spine density and inhibitory responses and normalized repetitive behavior. Conclusions: These findings suggest that cellular abnormalities in the DMS are associated with maladaptive repetitive behaviors and that manipulating PNNs can restore normal levels of repetitive behavior while altering DMS dendritic spines and inhibitory signaling.

Working Memory Is Complex and Dynamic, Like Your Thoughts.

Working memory is where thoughts are held and manipulated. For many years, the dominant model was that working memory relied on steady-state neural dynamics. A neural representation was activated and then held in that state. However, as often happens, the more we examine working memory (especially with new technology), the more complex it looks. Recent discoveries show that working memory involves multiple mechanisms, including discontinuous bouts of spiking. Memories are also dynamic, evolving in a task-dependent manner. Cortical rhythms may control those dynamics, thereby endowing top-down "executive" control over our thoughts.

Reduced variability of bursting activity during working memory.

Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, which may not be correct. Thus, we analyzed spiking and LFPs from monkeys' prefrontal cortex (PFC) to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could largely be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.

Neural signature of flexible coding in prefrontal cortex.

The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength-and consequently, the tuning of neurons-could change if features of a subsequent stimulus need to be "reassociated," i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.

A Goldilocks theory of cognitive control: Balancing precision and efficiency with low-dimensional control states.

Cognitive control orchestrates interactions between brain regions, guiding the transformation of information to support contextually appropriate and goal-directed behaviors. In this review, we propose a hierarchical model of cognitive control where low-dimensional control states direct the flow of high-dimensional representations between regions. This allows cognitive control to flexibly adapt to new environments and maintain the representational capacity to capture the richness of the world.

In V1, attending is not learning to see.

Learning and attention improve perception by increasing information about a stimulus in the neural population. In this issue of Neuron, Poort et al. investigate the circuit mechanisms underlying attention and learning, finding they work through different mechanisms.

Balancing Flexibility and Interference in Working Memory.

Working memory is central to cognition, flexibly holding the variety of thoughts needed for complex behavior. Yet, despite its importance, working memory has a severely limited capacity, holding only three to four items at once. In this article, I review experimental and computational evidence that the flexibility and limited capacity of working memory reflect the same underlying neural mechanism. I argue that working memory relies on interactions between high-dimensional, integrative representations in the prefrontal cortex and structured representations in the sensory cortex. Together, these interactions allow working memory to flexibly maintain arbitrary representations. However, the distributed nature of working memory comes at the cost of causing interference between items in memory, resulting in a limited capacity. Finally, I discuss several mechanisms used by the brain to reduce interference and maximize the effective capacity of working memory.

Shared mechanisms underlie the control of working memory and attention.

Cognitive control guides behaviour by controlling what, when, and how information is represented in the brain1. For example, attention controls sensory processing; top-down signals from prefrontal and parietal cortex strengthen the representation of task-relevant stimuli2-4. A similar 'selection' mechanism is thought to control the representations held 'in mind'-in working memory5-10. Here we show that shared neural mechanisms underlie the selection of items from working memory and attention to sensory stimuli. We trained rhesus monkeys to switch between two tasks, either selecting one item from a set of items held in working memory or attending to one stimulus from a set of visual stimuli. Neural recordings showed that similar representations in prefrontal cortex encoded the control of both selection and attention, suggesting that prefrontal cortex acts as a domain-general controller. By contrast, both attention and selection were represented independently in parietal and visual cortex. Both selection and attention facilitated behaviour by enhancing and transforming the representation of the selected memory or attended stimulus. Specifically, during the selection task, memory items were initially represented in independent subspaces of neural activity in prefrontal cortex. Selecting an item caused its representation to transform from its own subspace to a new subspace used to guide behaviour. A similar transformation occurred for attention. Our results suggest that prefrontal cortex controls cognition by dynamically transforming representations to control what and when cognitive computations are engaged.

Rotational dynamics reduce interference between sensory and memory representations.

Cognition depends on integrating sensory percepts with the memory of recent stimuli. However, the distributed nature of neural coding can lead to interference between sensory and memory representations. Here, we show that the brain mitigates such interference by rotating sensory representations into orthogonal memory representations over time. To study how sensory inputs and memories are represented, we recorded neurons from the auditory cortex of mice as they implicitly learned sequences of sounds. We found that the neural population represented sensory inputs and the memory of recent stimuli in two orthogonal dimensions. The transformation of sensory information into a memory was facilitated by a combination of 'stable' neurons, which maintained their selectivity over time, and 'switching' neurons, which inverted their selectivity over time. Together, these neural responses rotated the population representation, transforming sensory inputs into memory. Theoretical modeling showed that this rotational dynamic is an efficient mechanism for generating orthogonal representations, thereby protecting memories from sensory interference.

Is Activity Silent Working Memory Simply Episodic Memory?

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

Learning to control the brain through adaptive closed-loop patterned stimulation.

OBJECTIVE: Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. APPROACH: The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5-15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4-16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. MAIN RESULTS: In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. SIGNIFICANCE: Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.

Delay-period activity in frontal, parietal, and occipital cortex tracks noise and biases in visual working memory.

Working memory is imprecise, and these imprecisions can be explained by the combined influences of random diffusive error and systematic drift toward a set of stable states ("attractors"). However, the neural correlates of diffusion and drift remain unknown. Here, we investigated how delay-period activity in frontal and parietal cortex, which is known to correlate with the decline in behavioral memory precision observed with increasing memory load, might relate to diffusion and drift. We analyzed data from an existing experiment in which subjects performed delayed recall for line orientation, at different loads, during functional magnetic resonance imaging (fMRI) scanning. To quantify the influence of drift and diffusion, we modeled subjects' behavior using a discrete attractor model and calculated within-subject correlation between frontal and parietal delay-period activity and whole-trial estimates of drift and diffusion. We found that although increases in frontal and parietal activity were associated with increases in both diffusion and drift, diffusion explained the most variance in frontal and parietal delay-period activity. In comparison, a subsequent whole-brain regression analysis showed that drift, rather than diffusion, explained the most variance in delay-period activity in lateral occipital cortex. These results are consistent with a model of the differential recruitment of general frontoparietal mechanisms in response to diffusive noise and of stimulus-specific biases in occipital cortex.