Reorganization between preparatory and movement population responses in motor cortex

A geometric framework for understanding dynamic information integration in context-dependent computation

The prefrontal cortex (PFC) plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here, we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks replicated key experimentally observed features in the PFC of rodent and monkey experiments, such as mixed selectivity, neuronal sequential activity, and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision-making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance, and integration, yielding new insight into the computational mechanisms of context-dependent computation.

Largely shared neural codes for biological and nonbiological observed movements but not for executed actions in monkey premotor areas

The neural processing of others' observed actions recruits a large network of brain regions (the action observation network, AON), in which frontal motor areas are thought to play a crucial role. Since the discovery of mirror neurons (MNs) in the ventral premotor cortex, it has been assumed that their activation was conditional upon the presentation of biological rather than nonbiological motion stimuli, supporting a form of direct visuomotor matching. Nonetheless, nonbiological observed movements have rarely been used as control stimuli to evaluate visual specificity, thereby leaving the issue of similarity among neural codes for executed actions and biological or nonbiological observed movements unresolved. Here, we addressed this issue by recording from two nodes of the AON that are attracting increasing interest, namely the ventro-rostral part of the dorsal premotor area F2 and the mesial pre-supplementary motor area F6 of macaques while they 1) executed a reaching-grasping task, 2) observed an experimenter performing the task, and 3) observed a nonbiological effector moving in the same context. Our findings revealed stronger neuronal responses to the observation of biological than nonbiological movement, but biological and nonbiological visual stimuli produced highly similar neural dynamics and relied on largely shared neural codes, which in turn remarkably differed from those associated with executed actions. These results indicate that, in highly familiar contexts, visuo-motor remapping processes in premotor areas hosting MNs are more complex and flexible than predicted by a direct visuomotor matching hypothesis.

Computation Through Neural Population Dynamics

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Cerebellar granule cell axons support high-dimensional representations

In classical theories of cerebellar cortex, high-dimensional sensorimotor representations are used to separate neuronal activity patterns, improving associative learning and motor performance. Recent experimental studies suggest that cerebellar granule cell (GrC) population activity is low-dimensional. To examine sensorimotor representations from the point of view of downstream Purkinje cell 'decoders', we used three-dimensional acousto-optic lens two-photon microscopy to record from hundreds of GrC axons. Here we show that GrC axon population activity is high dimensional and distributed with little fine-scale spatial structure during spontaneous behaviors. Moreover, distinct behavioral states are represented along orthogonal dimensions in neuronal activity space. These results suggest that the cerebellar cortex supports high-dimensional representations and segregates behavioral state-dependent computations into orthogonal subspaces, as reported in the neocortex. Our findings match the predictions of cerebellar pattern separation theories and suggest that the cerebellum and neocortex use population codes with common features, despite their vastly different circuit structures.

When the cerebellum holds the starting gun

How the cerebellum affects movement onset is poorly understood. In this issue of Neuron, Dacre et al. (2021) establish that in the context of operant conditioning, the transient activation of the cerebello-thalamo-cortical pathway to the motor cortex is sufficient to initiate the conditioned movement.

Shared Dorsal Periaqueductal Gray Activation Patterns during Exposure to Innate and Conditioned Threats

The brainstem dorsal periaqueductal gray (dPAG) has been widely recognized as being a vital node orchestrating the responses to innate threats. Intriguingly, recent evidence also shows that the dPAG mediates defensive responses to fear conditioned contexts. However, it is unknown whether the dPAG displays independent or shared patterns of activation during exposure to innate and conditioned threats. It is also unclear how dPAG ensembles encode and predict diverse defensive behaviors. To address this question, we used miniaturized microscopes to obtain recordings of the same dPAG ensembles during exposure to a live predator and a fear conditioned context in male mice. dPAG ensembles encoded not only distance to threat, but also relevant features, such as predator speed and angular offset between mouse and threat. Furthermore, dPAG cells accurately encoded numerous defensive behaviors, including freezing, stretch-attend postures, and escape. Encoding of behaviors and of distance to threat occurred independently in dPAG cells. dPAG cells also displayed a shared representation to encode these behaviors and distance to threat across innate and conditioned threats. Last, we also show that escape could be predicted by dPAG activity several seconds in advance. Thus, dPAG activity dynamically tracks key kinematic and behavioral variables during exposure to threats, and exhibits similar patterns of activation during defensive behaviors elicited by innate or conditioned threats. These data indicate that a common pathway may be recruited by the dPAG during exposure to a wide variety of threat modalities.SIGNIFICANCE STATEMENT The dorsal periaqueductal gray (dPAG) is critical to generate defensive behaviors during encounters with threats of multiple modalities. Here we use longitudinal calcium transient recordings of dPAG ensembles in freely moving mice to show that this region uses shared patterns of activity to represent distance to an innate threat (a live predator) and a conditioned threat (a shock grid). We also show that dPAG neural activity can predict diverse defensive behaviors. These data indicate the dPAG uses conserved population-level activity patterns to encode and coordinate defensive behaviors during exposure to both innate and conditioned threats.

Thalamic control of cortical dynamics in a model of flexible motor sequencing

The neural mechanisms that generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. We model the basal ganglia inhibitory patterns as silencing some thalamic neurons while leaving others disinhibited and free to interact with cortex during specific motifs. We show that a small number of disinhibited thalamic neurons can control cortical dynamics to generate specific motor output in a noise-robust way. Additionally, a single "preparatory" thalamocortical network can produce fast cortical dynamics that support rapid transitions between any pair of learned motifs. If the thalamic units associated with each sequence component are segregated, many motor outputs can be learned without interference and then combined in arbitrary orders for the flexible production of long and complex motor sequences.

Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model

Across a range of motor and cognitive tasks, cortical activity can be accurately described by low-dimensional dynamics unfolding from specific initial conditions on every trial. These "preparatory states" largely determine the subsequent evolution of both neural activity and behavior, and their importance raises questions regarding how they are, or ought to be, set. Here, we formulate motor preparation as optimal anticipatory control of future movements and show that the solution requires a form of internal feedback control of cortical circuit dynamics. In contrast to a simple feedforward strategy, feedback control enables fast movement preparation by selectively controlling the cortical state in the small subspace that matters for the upcoming movement. Feedback but not feedforward control explains the orthogonality between preparatory and movement activity observed in reaching monkeys. We propose a circuit model in which optimal preparatory control is implemented as a thalamo-cortical loop gated by the basal ganglia.

Auditory decisions in the supplementary motor area

In human speech and communication across various species, recognizing and categorizing sounds is fundamental for the selection of appropriate behaviors. However, how does the brain decide which action to perform based on sounds? We explored whether the supplementary motor area (SMA), responsible for linking sensory information to motor programs, also accounts for auditory-driven decision making. To this end, we trained two rhesus monkeys to discriminate between numerous naturalistic sounds and words learned as target (T) or non-target (nT) categories. We found that the SMA at single and population neuronal levels perform decision-related computations that transition from auditory to movement representations in this task. Moreover, we demonstrated that the neural population is organized orthogonally during the auditory and the movement periods, implying that the SMA performs different computations. In conclusion, our results suggest that the SMA integrates acoustic information in order to form categorical signals that drive behavior.

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.

Independent generation of sequence elements by motor cortex

Rapid execution of motor sequences is believed to depend on fusing movement elements into cohesive units that are executed holistically. We sought to determine the contribution of primary motor and dorsal premotor cortex to this ability. Monkeys performed highly practiced two-reach sequences, interleaved with matched reaches performed alone or separated by a delay. We partitioned neural population activity into components pertaining to preparation, initiation and execution. The hypothesis that movement elements fuse makes specific predictions regarding all three forms of activity. We observed none of these predicted effects. Rapid two-reach sequences involved the same set of neural events as individual reaches but with preparation for the second reach occurring as the first was in flight. Thus, at the level of dorsal premotor and primary motor cortex, skillfully executing a rapid sequence depends not on fusing elements, but on the ability to perform two key processes at the same time.

The Planning Horizon for Movement Sequences

When performing a long chain of actions in rapid sequence, future movements need to be planned concurrently with ongoing action. However, how far ahead we plan, and whether this ability improves with practice, is currently unknown. Here, we designed an experiment in which healthy volunteers produced sequences of 14 finger presses quickly and accurately on a keyboard in response to numerical stimuli. On every trial, participants were only shown a fixed number of stimuli ahead of the current keypress. The size of this viewing window varied between 1 (next digit revealed with the pressing of the current key) and 14 (full view of the sequence). Participants practiced the task for 5 days, and their performance was continuously assessed on random sequences. Our results indicate that participants used the available visual information to plan multiple actions into the future, but that the planning horizon was limited: receiving information about more than three movements ahead did not result in faster sequence production. Over the course of practice, we found larger performance improvements for larger viewing windows and an expansion of the planning horizon. These findings suggest that the ability to plan future responses during ongoing movement constitutes an important aspect of skillful movement. Based on the results, we propose a framework to investigate the neuronal processes underlying simultaneous planning and execution.

Biochemical evidence accumulates across neurons to drive a network-level eruption

Neural network computations are usually assumed to emerge from patterns of fast electrical activity. Challenging this view, we show that a male fly's decision to persist in mating hinges on a biochemical computation that enables processing over minutes to hours. Each neuron in a recurrent network contains slightly different internal molecular estimates of mating progress. Protein kinase A (PKA) activity contrasts this internal measurement with input from the other neurons to represent accumulated evidence that the goal of the network has been achieved. When consensus is reached, PKA pushes the network toward a large-scale and synchronized burst of calcium influx that we call an eruption. Eruptions transform continuous deliberation within the network into an all-or-nothing output, after which the male will no longer sacrifice his life to continue mating. Here, biochemical activity, invisible to most large-scale recording techniques, is the key computational currency directing behavior and motivational state.

Transient deactivation of dorsal premotor cortex or parietal area 5 impairs feedback control of the limb in macaques

We can generate goal-directed motor corrections with surprising speed, but their neural basis is poorly understood. Here, we show that temporary cooling of dorsal premotor cortex (PMd) impaired both spatial accuracy and the speed of corrective responses, whereas cooling parietal area 5 (A5) impaired only spatial accuracy. Simulations based on optimal feedback control (OFC) models demonstrated that "deactivation" of the control policy (reduction in feedback gain) and state estimation (reduction in Kalman gain) caused impairments similar to that observed for PMd and A5 cooling, respectively. Furthermore, combined deactivation of both cortical regions led to additive impairments of individual deactivations, whereas reducing the amount of cooling to PMd led to impairments in response speed but not spatial accuracy, both also predicted by OFC models. These results provide causal support that frontoparietal circuits beyond primary somatosensory and motor cortices are involved in generating goal-directed motor corrections.

Extracting single-trial neural interaction using latent dynamical systems model

In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model.

Theta Synchrony Is Increased near Neural Populations That Are Active When Initiating Instructed Movement

Theta oscillations (3–8 Hz) in the human brain have been linked to perception, cognitive control, and spatial memory, but their relation to the motor system is less clear. We tested the hypothesis that theta oscillations coordinate distributed behaviorally relevant neural representations during movement using intracranial electroencephalography (iEEG) recordings from nine patients (n = 490 electrodes) as they performed a simple instructed movement task. Using high frequency activity (HFA; 70–200 Hz) as a marker of local spiking activity, we identified electrodes that were positioned near neural populations that showed increased activity during instruction and movement. We found that theta synchrony was widespread throughout the brain but was increased near regions that showed movement-related increases in neural activity. These results support the view that theta oscillations represent a general property of brain activity that may also play a specific role in coordinating widespread neural activity when initiating voluntary movement.

Neural manifold under plasticity in a goal driven learning behaviour

Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.

The Readiness Potential reflects the internal source of action, rather than decision uncertainty

Voluntary actions are preceded by a Readiness Potential (RP), a slow EEG (electroencephalogram) component generated in medial frontal cortical areas. The RP is classically thought to be specific to internally-driven decisions to act, and to reflect post-decision motor preparation. Recent work suggests instead that it may reflect noise or conflict during the decision itself, with internally driven decisions tending to be more random, more conflicted and thus more uncertain than externally driven actions. To contrast accounts based on endogenicity with accounts based on uncertainty, we recorded EEG in a task where participants decided to act or withhold action to accept or reject visually presented gambles, and used multivariate methods to extract an RP-like component. We found no difference in amplitude of this component between actions driven by strong versus weak evidence, suggesting that the RP may not reflect uncertainty. In contrast, the same RP-like component showed higher amplitudes prior to actions performed without any external evidence (guesses) than for actions performed in response to equivocal, conflicting evidence. This supports the view that the RP reflects the internal source of action, rather than decision uncertainty.

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.

Cortico-cerebellar interactions during goal-directed behavior

Preparatory activity is observed across multiple interconnected brain regions before goal-directed movement. Preparatory activity reflects discrete activity states representing specific future actions. It is unclear how this activity is mediated by multi-regional interactions. Recent evidence suggests that the cerebellum, classically associated with fine motor control, contributes to preparatory activity in the neocortex. We review recent advances and offer perspective on the function of cortico-cerebellar interactions during goal-directed behavior. We propose that the cerebellum learns to facilitate transitions between neocortical activity states. Transitions between activity states enable flexible and appropriately timed behavioral responses.

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.

The Readiness Potential reflects planning-based expectation, not uncertainty, in the timing of action

Actions are guided by a combination of external cues, internal intentions, and stored knowledge. Self-initiated voluntary actions, produced without immediate external cues, may be preceded by a slow EEG Readiness Potential (RP) that progressively increases prior to action. The cognitive significance of this neural event is controversial. Some accounts link the RP to the fact that timing of voluntary actions is generated endogenously, without external constraints. Others link it to the unique role of a planning process, and therefore of temporal expectation, in voluntary actions. In many previous experiments, actions are unconstrained by external cues, but also potentially involve preplanning and anticipation. To separate these factors, we developed a reinforcement learning paradigm where participants learned, through trial and error, the optimal time to act. If the RP reflects freedom from external constraint, its amplitude should be greater early in learning, when participants do not yet know when to act. Conversely, if the RP reflects planning, it should be greater later on, when participants have learned, and plan in advance, the time of action. We found that RP amplitudes grew with learning, suggesting that this neural activity reflects planning and anticipation for the forthcoming action, rather than freedom from external constraint.

Primary motor cortex in Parkinson's disease: Functional changes and opportunities for neurostimulation

Movement abnormalities of Parkinson's disease (PD) arise from disordered neural activity in multiple interconnected brain structures. The planning and execution of movement requires recruitment of a heterogeneous collection of pyramidal projection neurons in the primary motor cortex (M1). The neural representations of movement in M1 single-cell and field potential recordings are directly and indirectly influenced by the midbrain dopaminergic neurons that degenerate in PD. This review examines M1 functional alterations in PD as uncovered by electrophysiological recordings and neurostimulation studies in patients and experimental animal models. Dysfunction of the parkinsonian M1 depends on the severity and/or duration of dopamine-depletion and the species examined, and is expressed as alterations in movement-related firing dynamics; functional reorganisation of local circuits; and changes in field potential beta oscillations. Neurostimulation methods that modulate M1 activity directly (e.g., transcranial magnetic stimulation) or indirectly (subthalamic nucleus deep brain stimulation) improve motor function in PD patients, showing that targeted neuromodulation of M1 is a realistic therapy. We argue that the therapeutic profile of M1 neurostimulation is likely to be greatly enhanced with alternative technologies that permit cell-type specific control and incorporate feedback from electrophysiological biomarkers measured locally.

From motor planning to execution: a sensorimotor loop perspective

How is an evanescent wish to move translated into a concrete action? This simple question and puzzling miracle remains a focal point of motor systems neuroscience. Where does the difficulty lie? A great deal has been known about biomechanics for quite some time. More recently, there have been significant advances in our understanding of how the spinal system is organized into modules corresponding to spinal synergies, which are fixed patterns of multimuscle recruitment. But much less is known about how the supraspinal system recruits these synergies in the correct spatiotemporal pattern to effectively control movement. We argue that what makes the problem of supraspinal control so difficult is that it emerges as a result of multiple convergent and redundant sensorimotor loops. Because these loops are convergent, multiple modes of information are mixed before being sent to the spinal system; because they are redundant, information is overlapping such that a mechanism must exist to eliminate the redundancy before the signal is sent to the spinal system. Given these complex interactions, simple correlation analyses between movement variables and neural activity are likely to render a confusing and inconsistent picture. Here, we suggest that the perspective of sensorimotor loops might help in achieving a better systems-level understanding. Furthermore, state-of-the-art techniques in neurotechnology, such as optogenetics, appear to be well suited for investigating the problem of motor control at the level of loops.

Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making

Recent work has suggested that prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. Here we address that role using a new method for identifying task-relevant dimensions of neural population activity. Specifically, we show that PFC has a multi-dimensional code for context, decisions, and both relevant and irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases, and show that they do not arise from distinct populations, but of a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a plateau in decoding accuracy, revealing that rotational dynamics in PFC preserve sensory choice information for the duration of the stimulus integration period.

The Psychology of Reaching: Action Selection, Movement Implementation, and Sensorimotor Learning

The study of motor planning and learning in humans has undergone a dramatic transformation in the 20 years since this journal's last review of this topic. The behavioral analysis of movement, the foundational approach for psychology, has been complemented by ideas from control theory, computer science, statistics, and, most notably, neuroscience. The result of this interdisciplinary approach has been a focus on the computational level of analysis, leading to the development of mechanistic models at the psychological level to explain how humans plan, execute, and consolidate skilled reaching movements. This review emphasizes new perspectives on action selection and motor planning, research that stands in contrast to the previously dominant representation-based perspective of motor programming, as well as an emerging literature highlighting the convergent operation of multiple processes in sensorimotor learning.

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

Working memory (WM) is important for guiding behaviour, but not always for the next possible action. Here we define a WM item that is currently relevant for guiding behaviour as the functionally "active" item; whereas items maintained in WM, but not immediately relevant to behaviour, are defined as functionally "latent". Traditional neurophysiological theories of WM proposed that content is maintained via persistent neural activity (e.g., stable attractors); however, more recent theories have highlighted the potential role for "activity-silent" mechanisms (e.g., short-term synaptic plasticity). Given these somewhat parallel dichotomies, functionally active and latent cognitive states of WM have been associated with storage based on persistent-activity and activity-silent neural mechanisms, respectively. However, in this article we caution against a one-to-one correspondence between functional and activity states. We argue that the principal theoretical requirement for active and latent WM is that the corresponding neural states play qualitatively different functional roles. We consider a number of candidate solutions, and conclude that the neurophysiological mechanisms for functionally active and latent WM items are theoretically independent of the distinction between persistent activity-based and activity-silent forms of WM storage.

Activity in Primary Motor Cortex Related to Visual Feedback

Neural modulation in primate motor cortex exhibits complex patterns. We found that modulation during reaching could be separated into discrete temporal epochs. To determine if these epochs are driven by behavioral events, monkeys performed variations of a center-out reaching task. Monkeys viewed a computer cursor matched to hand position and a radial target at 1 of 16 locations. In some trials, they performed a visuomotor rotation (the cursor moved at an angle to the hand). After adaptation, encoding changes for single units are temporally structured: adaptation could affect one temporal component of a unit's response but not another. In half the normal and perturbed trials, we removed visual feedback before movement. Adaptation-sensitive firing components toward the end of movement are often weak or absent during reaches without feedback. These results show that temporal structure in motor cortical activity is driven by behavior, with a discrete component related to visual feedback.

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.

Principles of Corticocortical Communication: Proposed Schemes and Design Considerations

Nearly all brain functions involve routing neural activity among a distributed network of areas. Understanding this routing requires more than a description of interareal anatomical connectivity: it requires understanding what controls the flow of signals through interareal circuitry and how this communication might be modulated to allow flexible behavior. Here we review proposals of how communication, particularly between visual cortical areas, is instantiated and modulated, highlighting recent work that offers new perspectives. We suggest transitioning from a focus on assessing changes in the strength of interareal interactions, as often seen in studies of interareal communication, to a broader consideration of how different signaling schemes might contribute to computation. To this end, we discuss a set of features that might be desirable for a communication scheme.

Maintained Representations of the Ipsilateral and Contralateral Limbs during Bimanual Control in Primary Motor Cortex

Primary motor cortex (M1) almost exclusively controls the contralateral side of the body. However, M1 activity is also modulated during ipsilateral body movements. Previous work has shown that M1 activity related to the ipsilateral arm is independent of the M1 activity related to the contralateral arm. How do these patterns of activity interact when both arms move simultaneously? We explored this problem by training 2 monkeys (male, Macaca mulatta) in a postural perturbation task while recording from M1. Loads were applied to one arm at a time (unimanual) or both arms simultaneously (bimanual). We found 83% of neurons (n = 236) were responsive to both the unimanual and bimanual loads. We also observed a small reduction in activity magnitude during the bimanual loads for both limbs (25%). Across the unimanual and bimanual loads, neurons largely maintained their preferred load directions. However, there was a larger change in the preferred loads for the ipsilateral limb (∼25%) than the contralateral limb (∼9%). Lastly, we identified the contralateral and ipsilateral subspaces during the unimanual loads and found they captured a significant amount of the variance during the bimanual loads. However, the subspace captured more of the bimanual variance related to the contralateral limb (97%) than the ipsilateral limb (66%). Our results highlight that, even during bimanual motor actions, M1 largely retains its representations of the contralateral and ipsilateral limbs.SIGNIFICANCE STATEMENT Previous work has shown that primary motor cortex (M1) represents information related to the contralateral limb, its downstream target, but also reflects information related to the ipsilateral limb. Can M1 still represent both sources of information when performing simultaneous movements of the limbs? Here we record from M1 during a postural perturbation task. We show that activity related to the contralateral limb is maintained between unimanual and bimanual motor actions, whereas the activity related to the ipsilateral limb undergoes a small change between unimanual and bimanual motor actions. Our results indicate that two independent representations can be maintained and expressed simultaneously in M1.

A TMS study of preparatory suppression in binge drinkers

Binge drinking consists in a pattern of consumption characterised by the repeated alternation between massive alcohol intakes and abstinence periods. A continuum hypothesis suggests that this drinking endeavour represents an early stage of alcohol dependence rather than a separate phenomenon. Among the variety of alterations in alcohol-dependent individuals (ADIs), one has to do with the motor system, which does not show a normal pattern of activity during action preparation. In healthy controls (HCs), motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) over primary motor cortex (M1) show both facilitation and suppression effects, depending on the time and setting of TMS during action preparation. A recent study focusing on the suppression component revealed that this aspect of preparatory activity is abnormally weak in ADIs and that this defect scales with the risk of relapse. In the present study, we tested whether binge drinkers (BDs) present a similar deficit. To do so, we recorded MEPs in a set of hand muscles applying TMS in 20 BDs and in 20 matched HCs while they were preparing index finger responses in an instructed-delay choice reaction time task. Consistent with past research, the MEP data in HCs revealed a strong MEP suppression in this task. This effect was evident in all hand muscles, regardless of whether they were relevant or irrelevant in the task. BDs also showed some preparatory suppression, yet this effect was less consistent, especially in the prime mover of the responding hand. These findings suggest abnormal preparatory activity in BDs, similar to alcohol-dependent patients, though some of the current results also raise new questions regarding the significance of these observations.

Dynamic coordination of the perirhinal cortical neurons supports coherent representations between task epochs

Cortical neurons show distinct firing patterns across multiple task epochs characterized by different computations. Recent studies suggest that such distinct patterns underlie dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how coherent single-neuron code contributes to dynamic population code, we analyzed neural responses in the rat perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice directions between those epochs. By using principal component analysis as a population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordination across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of the individual neurons. These results demonstrated heterogeneity of coherent single-neuron representations in their contributions to population code.

Value and choice as separable and stable representations in orbitofrontal cortex

Value-based decision-making requires different variables-including offer value, choice, expected outcome, and recent history-at different times in the decision process. Orbitofrontal cortex (OFC) is implicated in value-based decision-making, but it is unclear how downstream circuits read out complex OFC responses into separate representations of the relevant variables to support distinct functions at specific times. We recorded from single OFC neurons while macaque monkeys made cost-benefit decisions. Using a novel analysis, we find separable neural dimensions that selectively represent the value, choice, and expected reward of the present and previous offers. The representations are generally stable during periods of behavioral relevance, then transition abruptly at key task events and between trials. Applying new statistical methods, we show that the sensitivity, specificity and stability of the representations are greater than expected from the population's low-level features-dimensionality and temporal smoothness-alone. The separability and stability suggest a mechanism-linear summation over static synaptic weights-by which downstream circuits can select for specific variables at specific times.

Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons

Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action observation, in the absence of any movement. A population-level understanding of this phenomenon is currently lacking. We recorded from single neurons, including identified PTNs, in (M1) (n = 187), and F5 (n = 115) as two adult male macaques executed, observed, or withheld (NoGo) reach-to-grasp actions. F5 maintained a similar representation of grasping actions during both execution and observation. In contrast, although many individual M1 neurons were active during observation, M1 population activity was distinct from execution, and more closely aligned to NoGo activity, suggesting this activity contributes to withholding of self-movement. M1 and its outputs may dissociate initiation of movement from representation of grasp in order to flexibly guide behaviour.

Coding Perceptual Decisions: From Single Units to Emergent Signaling Properties in Cortical Circuits

Spiking activity in single neurons of the primate visual cortex has been tightly linked to perceptual decisions. Any mechanism that reads out these perceptual signals to support behavior must respect the underlying neuroanatomy that shapes the functional properties of sensory neurons. Spatial distribution and timing of inputs to the next processing levels are critical, as conjoint activity of precursor neurons increases the spiking rate of downstream neurons and ultimately drives behavior. I set out how correlated activity might coalesce into a micropool of task-sensitive neurons signaling a particular percept to determine perceptual decision signals locally and for flexible interarea transmission depending on the task context. As data from more and more neurons and their complex interactions are analyzed, the space of computational mechanisms must be constrained based on what is plausible within neurobiological limits. This review outlines experiments to test the new perspectives offered by these extended methods.

Globus pallidus dynamics reveal covert strategies for behavioral inhibition

Flexible behavior requires restraint of actions that are no longer appropriate. This behavioral inhibition critically relies on frontal cortex - basal ganglia circuits. Within the basal ganglia, the globus pallidus pars externa (GPe) has been hypothesized to mediate selective proactive inhibition: being prepared to stop a specific action, if needed. Here we investigate population dynamics of rat GPe neurons during preparation-to-stop, stopping, and going. Rats selectively engaged proactive inhibition towards specific actions, as shown by slowed reaction times (RTs). Under proactive inhibition, GPe population activity occupied state-space locations farther from the trajectory followed during normal movement initiation. Furthermore, the state-space locations were predictive of distinct types of errors: failures-to-stop, failures-to-go, and incorrect choices. Slowed RTs on correct proactive trials reflected starting bias towards the alternative action, which was overcome before progressing towards action initiation. Our results demonstrate that rats can exert cognitive control via strategic adjustments to their GPe network state.

Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation

The supplementary motor area (SMA) is believed to contribute to higher order aspects of motor control. We considered a key higher order role: tracking progress throughout an action. We propose that doing so requires population activity to display low "trajectory divergence": situations with different future motor outputs should be distinct, even when present motor output is identical. We examined neural activity in SMA and primary motor cortex (M1) as monkeys cycled various distances through a virtual environment. SMA exhibited multiple response features that were absent in M1. At the single-neuron level, these included ramping firing rates and cycle-specific responses. At the population level, they included a helical population-trajectory geometry with shifts in the occupied subspace as movement unfolded. These diverse features all served to reduce trajectory divergence, which was much lower in SMA versus M1. Analogous population-trajectory geometry, also with low divergence, naturally arose in networks trained to internally guide multi-cycle movement.

Movement Decomposition in the Primary Motor Cortex

A complex action can be described as the composition of a set of elementary movements. While both kinematic and dynamic elements have been proposed to compose complex actions, the structure of movement decomposition and its neural representation remain unknown. Here, we examined movement decomposition by modeling the temporal dynamics of neural populations in the primary motor cortex of macaque monkeys performing forelimb reaching movements. Using a hidden Markov model, we found that global transitions in the neural population activity are associated with a consistent segmentation of the behavioral output into acceleration and deceleration epochs with directional selectivity. Single cells exhibited modulation of firing rates between the kinematic epochs, with abrupt changes in spiking activity timed with the identified transitions. These results reveal distinct encoding of acceleration and deceleration phases at the level of M1, and point to a specific pattern of movement decomposition that arises from the underlying neural activity. A similar approach can be used to probe the structure of movement decomposition in different brain regions, possibly controlling different temporal scales, to reveal the hierarchical structure of movement composition.

Causal Role of Motor Preparation during Error-Driven Learning

Current theories suggest that an error-driven learning process updates trial-by-trial to facilitate motor adaptation. How this process interacts with motor cortical preparatory activity-which current models suggest plays a critical role in movement initiation-remains unknown. Here, we evaluated the role of motor preparation during visuomotor adaptation. We found that preparation time was inversely correlated to variance of errors on current trials and mean error on subsequent trials. We also found causal evidence that intracortical microstimulation during motor preparation was sufficient to disrupt learning. Surprisingly, stimulation did not affect current trials, but instead disrupted the update computation of a learning process, thereby affecting subsequent trials. This is consistent with a Bayesian estimation framework where the motor system reduces its learning rate by virtue of lowering error sensitivity when faced with uncertainty. This interaction between motor preparation and the error-driven learning system may facilitate new probes into mechanisms underlying trial-by-trial adaptation.

Grid coding, spatial representation, and navigation: Should we assume an isomorphism?

Grid cells provide a compelling example of a link between cellular activity and an abstract and difficult to define concept like space. Accordingly, a representational perspective on grid coding argues that neural grid coding underlies a fundamentally spatial metric. Recently, some theoretical proposals have suggested extending such a framework to nonspatial cognition as well, such as category learning. Here, we provide a critique of the frequently employed assumption of an isomorphism between patterns of neural activity (e.g., grid cells), mental representation, and behavior (e.g., navigation). Specifically, we question the strict isomorphism between these three levels and suggest that human spatial navigation is perhaps best characterized by a wide variety of both metric and nonmetric strategies. We offer an alternative perspective on how grid coding might relate to human spatial navigation, arguing that grid coding is part of a much larger conglomeration of neural activity patterns that dynamically tune to accomplish specific behavioral outputs.

Repetita iuvant: repetition facilitates online planning of sequential movements

Beyond being essential for long-term motor-skill development, movement repetition has immediate benefits on performance, increasing speed and accuracy of a second execution. While repetition effects have been reported for single reaching movements, it has yet to be determined whether they also occur for movement sequences, and what aspects of sequence production are improved. We addressed these questions in two behavioral experiments using a discrete sequence production (DSP) task in which human volunteers had to perform short sequences of finger movements. In experiment 1, we presented participants with randomly varying sequences and manipulated 1) whether the same sequence was repeated on successive trials and 2) whether participants had to execute the sequence (Go) or not (No-Go). We establish that sequence repetition led to immediate improvements in speed without associated accuracy costs. The largest benefit was observed in the middle part of a sequence, suggesting that sequence repetition facilitated online planning. This claim was further supported by experiment 2, in which we kept a set of sequences fixed throughout the experiment, thus allowing participants to develop sequence-specific learning: once the need for online planning decreased, the benefit of repetition disappeared. Finally, we found that repetition-related improvements only occurred for the trials that had been preceded by sequence production, suggesting that action selection and sequence preplanning may not be sufficient to reap the benefits of repetition. Together, these results show that repetition can enhance representations at the level of movement sequences (rather than of individual movements) and facilitate online planning.NEW & NOTEWORTHY Even for overlearned motor skills such as reaching, movement repetition improves performance. How brain processes associated with motor planning or execution benefit from repetition, however, remains unclear. We report the novel finding of repetition effects for sequential movements. Our results show that repetition benefits are tied to improved online planning of upcoming sequence elements. We also highlight how actual movement experience appears to be more beneficial than mental rehearsal for observing short-term repetition effects.

Mechanisms that allow cortical preparatory activity without inappropriate movement

We reveal a novel mechanism that explains how preparatory activity can evolve in motor-related cortical areas without prematurely inducing movement. The smooth eye movement region of the frontal eye fields (FEF_SEM) is a critical node in the neural circuit controlling smooth pursuit eye movement. Preparatory activity evolves in the monkey FEF_SEM during fixation in parallel with an objective measure of visual-motor gain. We propose that the use of FEF_SEM output as a gain signal rather than a movement command allows for preparation to progress in pursuit without causing movement. We also show that preparatory modulation of firing rate in FEF_SEM predicts movement, providing evidence against the ‘movement-null’ space hypothesis as an explanation of how preparatory activity can progress without movement. Finally, there is a partial reorganization of FEF_SEM population activity between preparation and movement that would allow for a directionally non-specific component of preparatory visual-motor gain enhancement in pursuit.

The Transition from Evaluation to Selection Involves Neural Subspace Reorganization in Core Reward Regions

Economic choice proceeds from evaluation, in which we contemplate options, to selection, in which we weigh options and choose one. These stages must be differentiated so that decision makers do not proceed to selection before evaluation is complete. We examined responses of neurons in two core reward regions, orbitofrontal (OFC) and ventromedial prefrontal cortex (vmPFC), during two-option choice with asynchronous offer presentation. Our data suggest that neurons selective during the first (presumed evaluation) and second (presumed comparison and selection) offer epochs come from a single pool. Stage transition is accompanied by a shift toward orthogonality in the low-dimensional population response manifold. Nonetheless, the relative position of each option in driving responses in the population subspace is preserved. The orthogonalization we observe supports the hypothesis that the transition from evaluation to selection leads to reorganization of response subspace and suggests a mechanism by which value-related signals are prevented from prematurely driving choice.

The neural mechanisms of face processing: cells, areas, networks, and models

Since its discovery, the face-processing network in the brain of the macaque monkey has emerged as a model system that allowed for major neural mechanisms of face recognition to be identified - with implications for object recognition at large. Populations of face cells encode faces through broad tuning curves, whose shapes change over time. Face representations differ qualitatively across faces areas, and we not only understand the global organization of these specializations, but also some of the transformations between face areas, both feed-forward and feed-back, and the computational principles behind face representations and transformations. Facial information is combined with physical features and mnemonic features in extensions of the core network, which forms an early part of the primate social brain.

Area 2 of primary somatosensory cortex encodes kinematics of the whole arm

Proprioception, the sense of body position, movement, and associated forces, remains poorly understood, despite its critical role in movement. Most studies of area 2, a proprioceptive area of somatosensory cortex, have simply compared neurons' activities to the movement of the hand through space. Using motion tracking, we sought to elaborate this relationship by characterizing how area 2 activity relates to whole arm movements. We found that a whole-arm model, unlike classic models, successfully predicted how features of neural activity changed as monkeys reached to targets in two workspaces. However, when we then evaluated this whole-arm model across active and passive movements, we found that many neurons did not consistently represent the whole arm over both conditions. These results suggest that 1) neural activity in area 2 includes representation of the whole arm during reaching and 2) many of these neurons represented limb state differently during active and passive movements.