Emergence of abstract rules in the primate brain.

Various aspects of human cognition are shaped and enriched by abstract rules, which help to describe, link and classify discrete events and experiences into meaningful concepts. However, where and how these entities emerge in the primate brain and the neuronal mechanisms underlying them remain the subject of extensive research and debate. Evidence from imaging studies in humans and single-neuron recordings in monkeys suggests a pivotal role for the prefrontal cortex in the representation of abstract rules; however, behavioural studies in lesioned monkeys and data from neuropsychological examinations of patients with prefrontal damage indicate substantial functional dissociations and task dependency in the contribution of prefrontal cortical regions to rule-guided behaviour. This Review describes our current understanding of the dynamic emergence of abstract rules in primate cognition, and of the distributed neural network that supports abstract rule formation, maintenance, revision and task-dependent implementation.

Posterior Parietal Cortex Plays a Causal Role in Abstract Memory-Based Visual Categorical Decisions.

Neural activity in the lateral intraparietal cortex (LIP) correlates with both sensory evaluation and motor planning underlying visuomotor decisions. We previously showed that LIP plays a causal role in visually-based perceptual and categorical decisions, and preferentially contributes to evaluating sensory stimuli over motor planning. In that study, however, monkeys reported their decisions with a saccade to a colored target associated with the correct motion category or direction. Since LIP is known to play a role in saccade planning, it remains unclear whether LIP's causal role in such decisions extend to decision-making tasks which do not involve saccades. Here, we employed reversible pharmacological inactivation of LIP neural activity while two male monkeys performed delayed match to category (DMC) and delayed match to sample (DMS) tasks. In both tasks, monkeys needed to maintain gaze fixation throughout the trial and report whether a test stimulus was a categorical match or nonmatch to the previous sample stimulus by releasing a touch bar. LIP inactivation impaired monkeys' behavioral performance in both tasks, with deficits in both accuracy and reaction time (RT). Furthermore, we recorded LIP neural activity in the DMC task targeting the same cortical locations as in the inactivation experiments. We found significant neural encoding of the sample category, which was correlated with monkeys' categorical decisions in the DMC task. Taken together, our results demonstrate that LIP plays a generalized role in visual categorical decisions independent of the task-structure and motor response modality.SIGNIFICANCE STATEMENT Neural activity in the lateral intraparietal cortex (LIP) correlates with perceptual and categorical decisions, in addition to its role in mediating saccadic eye movements. Past work found that LIP is causally involved in visual decisions that are rapidly reported by saccades in a reaction time based decision making task. Here we use reversible inactivation of LIP to test whether LIP is also causally involved in visual decisions when reported by hand movements during delayed matching tasks. Here we show that LIP inactivation impaired monkeys' task performance during both memory-based discrimination and categorization tasks. These results demonstrate that LIP plays a generalized role in visual categorical decisions independent of the task-structure and motor response modality.

Neuronal Mechanisms of Visual Categorization: An Abstract View on Decision Making.

Categorization is our ability to flexibly assign sensory stimuli into discrete, behaviorally relevant groupings. Categorical decisions can be used to study decision making more generally by dissociating category identity of stimuli from the actions subjects use to signal their decisions. Here we discuss the evidence for such abstract categorical encoding in the primate brain and consider the relationship with other perceptual decision paradigms. Recent work on visual categorization has examined neuronal activity across a hierarchically organized network of cortical areas in monkeys trained to group visual stimuli into arbitrary categories. This has revealed a transformation of visual-feature encoding in early visual cortical areas into more flexible categorical representations in downstream parietal and prefrontal areas. These neuronal category representations are encoded as abstract internal cognitive states because they are not rigidly linked with either specific sensory stimuli or the actions that the monkeys use to signal their categorical choices.

Redundant representations are required to disambiguate simultaneously presented complex stimuli.

A pedestrian crossing a street during rush hour often looks and listens for potential danger. When they hear several different horns, they localize the cars that are honking and decide whether or not they need to modify their motor plan. How does the pedestrian use this auditory information to pick out the corresponding cars in visual space? The integration of distributed representations like these is called the assignment problem, and it must be solved to integrate distinct representations across but also within sensory modalities. Here, we identify and analyze a solution to the assignment problem: the representation of one or more common stimulus features in pairs of relevant brain regions-for example, estimates of the spatial position of cars are represented in both the visual and auditory systems. We characterize how the reliability of this solution depends on different features of the stimulus set (e.g., the size of the set and the complexity of the stimuli) and the details of the split representations (e.g., the precision of each stimulus representation and the amount of overlapping information). Next, we implement this solution in a biologically plausible receptive field code and show how constraints on the number of neurons and spikes used by the code force the brain to navigate a tradeoff between local and catastrophic errors. We show that, when many spikes and neurons are available, representing stimuli from a single sensory modality can be done more reliably across multiple brain regions, despite the risk of assignment errors. Finally, we show that a feedforward neural network can learn the optimal solution to the assignment problem, even when it receives inputs in two distinct representational formats. We also discuss relevant results on assignment errors from the human working memory literature and show that several key predictions of our theory already have support.

Abstract Encoding of Categorical Decisions in Medial Superior Temporal and Lateral Intraparietal Cortices.

Categorization is an essential cognitive and perceptual process for decision-making and recognition. The posterior parietal cortex, particularly the lateral intraparietal (LIP) area has been suggested to transform visual feature encoding into abstract categorical representations. By contrast, areas closer to sensory input, such as the middle temporal (MT) area, encode stimulus features but not more abstract categorical information during categorization tasks. Here, we compare the contributions of the medial superior temporal (MST) and LIP areas in category computation by recording neuronal activity in both areas from two male rhesus macaques trained to perform a visual motion categorization task. MST is a core motion-processing region interconnected with MT and is often considered an intermediate processing stage between MT and LIP. We show that MST exhibits robust decision-correlated motion category encoding and working memory encoding similar to LIP, suggesting that MST plays a substantial role in cognitive computation, extending beyond its widely recognized role in visual motion processing.SIGNIFICANCE STATEMENT Categorization requires assigning incoming sensory stimuli into behaviorally relevant groups. Previous work found that parietal area LIP shows a strong encoding of the learned category membership of visual motion stimuli, while visual area MT shows strong direction tuning but not category tuning during a motion direction categorization task. Here we show that the medial superior temporal (MST) area, a visual motion-processing region interconnected with both LIP and MT, shows strong visual category encoding similar to that observed in LIP. This suggests that MST plays a greater role in abstract cognitive functions, extending beyond its well known role in visual motion processing.

Mice Preferentially Use Increases in Cerebral Cortex Spiking to Detect Changes in Visual Stimuli.

Whenever the retinal image changes, some neurons in visual cortex increase their rate of firing whereas others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice of both sexes to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements in spiking were generated in the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity has costs. This work clarifies neuronal decoding strategies used by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.SIGNIFICANCE STATEMENT Visual responses in the primary visual cortex (V1) are diverse, in that neurons can be either excited or inhibited by the onset of a visual stimulus. We selectively potentiated or suppressed V1 spiking in mice while they performed contrast change detection tasks. In other experiments, excitation or inhibition was delivered to V1 independent of visual stimuli. Mice readily detected increases in V1 spiking while equivalent reductions in V1 spiking suppressed the probability of detection, even when increases and decreases in V1 spiking were generated in the same neuronal populations. Our data raise the striking possibility that only increments in spiking are used to render information to structures downstream of V1.

Nonlinear mixed selectivity supports reliable neural computation.

Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinearly mixed feature representations have been observed throughout primary sensory, decision-making, and motor brain areas. In these areas, different features are almost always nonlinearly mixed to some degree, rather than represented separately or with only additive (linear) mixing, which we refer to as pure selectivity. Mixed selectivity has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that it has another important benefit: in many cases, it makes orders of magnitude fewer decoding errors than pure selectivity even when both forms of selectivity use the same number of spikes. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that mixed selectivity exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that nonlinear mixed selectivity may be a general coding scheme exploited by the brain for reliable and efficient neural computation.

Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization.

Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. This phenomenon is the result of "catastrophic forgetting," in which training an ANN disrupts connection weights that were important for solving previous tasks, degrading task performance. Several recent studies have proposed methods to stabilize connection weights of ANNs that are deemed most important for solving a task, which helps alleviate catastrophic forgetting. Here, drawing inspiration from algorithms that are believed to be implemented in vivo, we propose a complementary method: adding a context-dependent gating signal, such that only sparse, mostly nonoverlapping patterns of units are active for any one task. This method is easy to implement, requires little computational overhead, and allows ANNs to maintain high performance across large numbers of sequentially presented tasks, particularly when combined with weight stabilization. We show that this method works for both feedforward and recurrent network architectures, trained using either supervised or reinforcement-based learning. This suggests that using multiple, complementary methods, akin to what is believed to occur in the brain, can be a highly effective strategy to support continual learning.

Mnemonic Encoding and Cortical Organization in Parietal and Prefrontal Cortices.

Persistent activity within the frontoparietal network is consistently observed during tasks that require working memory. However, the neural circuit mechanisms underlying persistent neuronal encoding within this network remain unresolved. Here, we ask how neural circuits support persistent activity by examining population recordings from posterior parietal (PPC) and prefrontal (PFC) cortices in two male monkeys that performed spatial and motion direction-based tasks that required working memory. While spatially selective persistent activity was observed in both areas, robust selective persistent activity for motion direction was only observed in PFC. Crucially, we find that this difference between mnemonic encoding in PPC and PFC is associated with the presence of functional clustering: PPC and PFC neurons up to ∼700 µm apart preferred similar spatial locations, and PFC neurons up to ∼700 µm apart preferred similar motion directions. In contrast, motion-direction tuning similarity between nearby PPC neurons was much weaker and decayed rapidly beyond ∼200 µm. We also observed a similar association between persistent activity and functional clustering in trained recurrent neural network models embedded with a columnar topology. These results suggest that functional clustering facilitates mnemonic encoding of sensory information.SIGNIFICANCE STATEMENT Working memory refers to our ability to temporarily store and manipulate information. Numerous studies have observed that, during working memory, neurons in higher cortical areas, such as the parietal and prefrontal cortices, mnemonically encode the remembered stimulus. However, several recent studies have failed to observe mnemonic encoding during working memory, raising the question as to why mnemonic encoding is observed during some, but not all, conditions. In this study, we show that mnemonic encoding occurs when a cortical area is organized such that nearby neurons preferentially respond to the same stimulus. This result provides plausible neuronal conditions that allow for mnemonic encoding, and gives us further understanding of the brain's mechanisms that support working memory.

Task dependence of visual and category representations in prefrontal and inferior temporal cortices.

Visual categorization is an essential perceptual and cognitive process for assigning behavioral significance to incoming stimuli. Categorization depends on sensory processing of stimulus features as well as flexible cognitive processing for classifying stimuli according to the current behavioral context. Neurophysiological studies suggest that the prefrontal cortex (PFC) and the inferior temporal cortex (ITC) are involved in visual shape categorization. However, their precise roles in the perceptual and cognitive aspects of the categorization process are unclear, as the two areas have not been directly compared during changing task contexts. To address this, we examined the impact of task relevance on categorization-related activity in PFC and ITC by recording from both areas as monkeys alternated between a shape categorization and passive viewing tasks. As monkeys viewed the same stimuli in both tasks, the impact of task relevance on encoding in each area could be compared. While both areas showed task-dependent modulations of neuronal activity, the patterns of results differed markedly. PFC, but not ITC, neurons showed a modest increase in firing rates when stimuli were task relevant. PFC also showed significantly stronger category selectivity during the task compared with passive viewing, while task-dependent modulations of category selectivity in ITC were weak and occurred with a long latency. Finally, both areas showed an enhancement of stimulus selectivity during the task compared with passive viewing. Together, this suggests that the ITC and PFC show differing degrees of task-dependent flexibility and are preferentially involved in the perceptual and cognitive aspects of the categorization process, respectively.

A comparison of lateral and medial intraparietal areas during a visual categorization task.

Categorization is essential for interpreting sensory stimuli and guiding our actions. Recent studies have revealed robust neuronal category representations in the lateral intraparietal area (LIP). Here, we examine the specialization of LIP for categorization and the roles of other parietal areas by comparing LIP and the medial intraparietal area (MIP) during a visual categorization task. MIP is involved in goal-directed arm movements and visuomotor coordination but has not been implicated in non-motor cognitive functions, such as categorization. As expected, we found strong category encoding in LIP. Interestingly, we also observed category signals in MIP. However, category signals were stronger and appeared with a shorter latency in LIP than MIP. In this task, monkeys indicated whether a test stimulus was a category match to a previous sample with a manual response. Test-period activity in LIP showed category encoding and distinguished between matches and non-matches. In contrast, MIP primarily reflected the match/non-match status of test stimuli, with a strong preference for matches (which required a motor response). This suggests that, although category representations are distributed across parietal cortex, LIP and MIP play distinct roles: LIP appears more involved in the categorization process itself, whereas MIP is more closely tied to decision-related motor actions.

High-performance execution of psychophysical tasks with complex visual stimuli in MATLAB.

Behavioral, psychological, and physiological experiments often require the ability to present sensory stimuli, monitor and record subjects' responses, interface with a wide range of devices, and precisely control the timing of events within a behavioral task. Here, we describe our recent progress developing an accessible and full-featured software system for controlling such studies using the MATLAB environment. Compared with earlier reports on this software, key new features have been implemented to allow the presentation of more complex visual stimuli, increase temporal precision, and enhance user interaction. These features greatly improve the performance of the system and broaden its applicability to a wider range of possible experiments. This report describes these new features and improvements, current limitations, and quantifies the performance of the system in a real-world experimental setting.

Low-dimensional encoding of decisions in parietal cortex reflects long-term training history.

Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach-generalized multilinear models-to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys' performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks.

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

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

Lipomas of the Toes: An Eight-Case Series.

BACKGROUND: Lipomas, derived from adipose tissue, most frequently occur in the cephalic regions and proximal extremities, but rarely in the toes. We aimed to highlight the clinical features, diagnosis, and treatment of lipomas of the toes. METHODS: We analyzed 8 patients with lipomas of the toes who were diagnosed and treated during a 5-year period. RESULTS: Lipomas of the toes were equally distributed by sex. Patients ranged in age from 28 to 67 years (mean age, 51.75 years). Six patients (75%) had a single lesion, and all of the patients developed lipomas on the hallux. Most patients (75%) presented with a painless, subcutaneous, slow-growing mass. The duration from symptom onset to surgical excision ranged from 1 month to 20 years (mean, 52.75 months). Lipoma size varied from 0.4 to 3.9 cm in diameter (mean, 1.6 cm). Magnetic resonance imaging showed a well-encapsulated mass with hyperintense signal on T1-weighted images and hypointense signal on T2-weighted images. All of the patients were treated with surgical excision, and no recurrences were found at mean follow-up of 38.5 months. Six patients were diagnosed as having typical lipomas, one a fibrolipoma, and one a spindle cell lipoma, which needs to be differentiated from other benign and malignant lesions. CONCLUSIONS: Lipomas of the toes are rare, slow-growing, painless, subcutaneous tumors. Men and women are equally affected, usually in their 50s. Magnetic resonance imaging is the favored modality for presurgical diagnosis and planning. Complete surgical excision is the optimal treatment, with rare recurrence.

Schema formation in a neural population subspace underlies learning-to-learn in flexible sensorimotor problem-solving.

Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings known to depend on the prefrontal cortex. The network displayed an exponential time course of accelerated learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity; its reuse in new problems facilitates learning by limiting connection weight changes. Our work highlights the weight-driven modifications of the vector field, which determines the population trajectory of a recurrent network and behavior. Such plasticity is especially important for preserving and reusing the learned schema in spite of undesirable changes of the vector field due to the transition to learning a new problem; the accumulated changes across problems account for the learning-to-learn dynamics.

Primate superior colliculus is engaged in abstract higher-order cognition.

Categorization is a fundamental cognitive process by which the brain assigns stimuli to behaviorally meaningful groups. Investigations of visual categorization in primates have identified a hierarchy of cortical areas that are involved in the transformation of sensory information into abstract category representations. However, categorization behaviors are ubiquitous across diverse animal species, even those without a neocortex, motivating the possibility that subcortical regions may contribute to abstract cognition in primates. One candidate structure is the superior colliculus (SC), an evolutionarily conserved midbrain region that, although traditionally thought to mediate only reflexive spatial orienting, is involved in cognitive tasks that require spatial orienting. Here, we reveal a novel role of the primate SC in abstract, higher-order visual cognition. We compared neural activity in the SC and the posterior parietal cortex (PPC), a region previously shown to causally contribute to category decisions, while monkeys performed a visual categorization task in which they report their decisions with a hand movement. The SC exhibits stronger and shorter-latency category encoding than the PPC, and inactivation of the SC markedly impairs monkeys' category decisions. These results extend SC's established role in spatial orienting to abstract, non-spatial cognition.

Experience-dependent representation of visual categories in parietal cortex.

Categorization is a process by which the brain assigns meaning to sensory stimuli. Through experience, we learn to group stimuli into categories, such as 'chair', 'table' and 'vehicle', which are critical for rapidly and appropriately selecting behavioural responses. Although much is known about the neural representation of simple visual stimulus features (for example, orientation, direction and colour), relatively little is known about how the brain learns and encodes the meaning of stimuli. We trained monkeys to classify 360 degrees of visual motion directions into two discrete categories, and compared neuronal activity in the lateral intraparietal (LIP) and middle temporal (MT) areas, two interconnected brain regions known to be involved in visual motion processing. Here we show that neurons in LIP--an area known to be centrally involved in visuo-spatial attention, motor planning and decision-making-robustly reflect the category of motion direction as a result of learning. The activity of LIP neurons encoded directions of motion according to their category membership, and that encoding shifted after the monkeys were retrained to group the same stimuli into two new categories. In contrast, neurons in area MT were strongly direction selective but carried little, if any, explicit category information. This indicates that LIP might be an important nexus for the transformation of visual direction selectivity to more abstract representations that encode the behavioural relevance, or meaning, of stimuli.

Interaction between neuronal encoding and population dynamics during categorization task switching in parietal cortex.

Primates excel at categorization, a cognitive process for assigning stimuli into behaviorally relevant groups. Categories are encoded in multiple brain areas and tasks, yet it remains unclear how neural encoding and dynamics support cognitive tasks with different demands. We recorded from parietal cortex during flexible switching between categorization tasks with distinct cognitive and motor demands and also studied recurrent neural networks (RNNs) trained on the same tasks. In the one-interval categorization task (OIC), monkeys rapidly reported their decisions with a saccade. In the delayed match-to-category (DMC) task, monkeys decided whether sequentially presented stimuli were categorical matches. Neuronal category encoding generalized across tasks, but categorical encoding was more binary-like in the DMC task and more graded in the OIC task. Furthermore, analysis of trained RNNs supports the hypothesis that binary-like encoding in DMC arises through compression of graded feature encoding by attractor dynamics underlying stimulus maintenance and/or comparison in working memory.

Primate neuronal connections are sparse in cortex as compared to mouse.

Detailing how primate and mouse neurons differ is critical for creating generalized models of how neurons process information. We reconstruct 15,748 synapses in adult Rhesus macaques and mice and ask how connectivity differs on identified cell types in layer 2/3 of primary visual cortex. Primate excitatory and inhibitory neurons receive 2-5 times fewer excitatory and inhibitory synapses than similar mouse neurons. Primate excitatory neurons have lower excitatory-to-inhibitory (E/I) ratios than mouse but similar E/I ratios in inhibitory neurons. In both species, properties of inhibitory axons such as synapse size and frequency are unchanged, and inhibitory innervation of excitatory neurons is local and specific. Using artificial recurrent neural networks (RNNs) optimized for different cognitive tasks, we find that penalizing networks for creating and maintaining synapses, as opposed to neuronal firing, reduces the number of connections per node as the number of nodes increases, similar to primate neurons compared with mice.