Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex

Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information

When preparing a movement, we often rely on partial or incomplete information, which can decrement task performance. In behaving monkeys we show that the degree of cued target information is reflected in both, neural variability in motor cortex and behavioral reaction times. We study the underlying mechanisms in a spiking motor-cortical attractor model. By introducing a biologically realistic network topology where excitatory neuron clusters are locally balanced with inhibitory neuron clusters we robustly achieve metastable network activity across a wide range of network parameters. In application to the monkey task, the model performs target-specific action selection and accurately reproduces the task-epoch dependent reduction of trial-to-trial variability in vivo where the degree of reduction directly reflects the amount of processed target information, while spiking irregularity remained constant throughout the task. In the context of incomplete cue information, the increased target selection time of the model can explain increased behavioral reaction times. We conclude that context-dependent neural and behavioral variability is a signum of attractor computation in the motor cortex.

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Functional states of prelimbic and related circuits during the acquisition of a GO/noGO task in rats

GO/noGO tasks enable assessing decision-making processes and the ability to suppress a specific action according to the context. Here, rats had to discriminate between 2 visual stimuli (GO or noGO) shown on an iPad screen. The execution (for GO) or nonexecution (for noGO) of the selected action (to touch or not the visual display) were reinforced with food. The main goal was to record and to analyze local field potentials collected from cortical and subcortical structures when the visual stimuli were shown on the touch screen and during the subsequent activities. Rats were implanted with recording electrodes in the prelimbic cortex, primary motor cortex, nucleus accumbens septi, basolateral amygdala, dorsolateral and dorsomedial striatum, hippocampal CA1, and mediodorsal thalamic nucleus. Spectral analyses of the collected data demonstrate that the prelimbic cortex was selectively involved in the cognitive and motivational processing of the learning task but not in the execution of reward-directed behaviors. In addition, the other recorded structures presented specific tendencies to be involved in these 2 types of brain activity in response to the presentation of GO or noGO stimuli. Spectral analyses, spectrograms, and coherence between the recorded brain areas indicate their specific involvement in GO vs. noGO tasks.

Flexible decision-making is related to strategy learning, vicarious trial and error, and medial prefrontal rhythms during spatial set-shifting

Flexible decision-making requires a balance between exploring features of an environment and exploiting prior knowledge. Behavioral flexibility is typically measured by how long it takes subjects to consistently make accurate choices after reward contingencies switch or task rules change. This measure, however, only allows for tracking flexibility across multiple trials, and does not assess the degree of flexibility. Plus, although increases in decision-making accuracy are strong indicators of learning, other decision-making behaviors have also been suggested as markers of flexibility, such as the on-the-fly decision reversals known as vicarious trial and error (VTE) or switches to a different, but incorrect, strategy. We sought to relate flexibility, learning, and neural activity by comparing choice history-derived evaluation of strategy use with changes in decision-making accuracy and VTE behavior while recording from the medial prefrontal cortex (mPFC) in rats. Using a set-shifting task that required rats to repeatedly switch between spatial decision-making strategies, we show that a previously developed strategy likelihood estimation procedure could identify putative learning points based on decision history. We confirm the efficacy of learning point estimation by showing increases in decision-making accuracy aligned to the learning point. Additionally, we show increases in the rate of VTE behavior surrounding identified learning points. By calculating changes in strategy likelihoods across trials, we tracked flexibility on a trial-by-trial basis and show that flexibility scores also increased around learning points. Further, we demonstrate that VTE behaviors could be separated into indecisive and deliberative subtypes depending on whether they occurred during periods of high or low flexibility and whether they led to correct or incorrect choice outcomes. Field potential recordings from the mPFC during decisions exhibited increased beta band activity on trials with VTE compared to non-VTE trials, as well as increased gamma during periods when learned strategies could be exploited compared to prelearning, exploratory periods. This study demonstrates that increased behavioral flexibility and VTE rates are often aligned to task learning. These relationships can break down, however, suggesting that VTE is not always an indicator of deliberative decision-making. Additionally, we further implicate the mPFC in decision-making and learning by showing increased beta-based activity on VTE trials and increased gamma after learning.

Impact of prior SARS-CoV-2 infection on college students' hesitancy to receive additional COVID-19 vaccine booster doses: A study from Taizhou, China

Purpose

This study aimed to examine the impact of a history of SARS-CoV-2 infection on the hesitancy of college students to receive additional COVID-19 vaccine booster doses.

Methods

A population-based self-administered online survey was conducted in July 2024 in Taizhou, China. A total of 792 respondents were included in this study. Logistic regression was conducted to identify factors associated with college students' hesitation to receive booster doses of the COVID-19 vaccine.

Results

Of 792 respondents, 32.2 % hesitated to receive additional doses of the COVID-19 vaccine booster. Furthermore, 23.5 % of the respondents reported an increase in hesitancy to receiving additional COVID-19 vaccine booster doses compared to before they were infected with SARS-CoV-2. In the regression analyses, college students who had a secondary infection were more hesitant to receive additional COVID-19 vaccine booster doses (OR = 0.481, 95 % CI: (0.299-0.774), P = 0.003). Moreover, students with secondary infections who were male (OR = 0.417, 95 % CI: 0.221-0.784, P = 0.007), with lower than a bachelor's degree (OR = 0.471, 95 % CI: 0.272-0.815, P = 0.007), in non-medical majors (OR = 0.460, 95 % CI: 0.248-0.856, P = 0.014), and sophomores or below (OR = 0.483, 95 % CI: 0.286-0.817, P = 0.007) were more hesitant to receive additional COVID-19 vaccine booster doses.

Conclusion

A history of SARS-CoV-2 infection affects college students' hesitation to receive additional COVID-19 vaccine booster doses, which was higher in those who experienced secondary infections.

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Whole-brain neural substrates of behavioral variability in the larval zebrafish

Animals engaged in naturalistic behavior can exhibit a large degree of behavioral variability even under sensory invariant conditions. Such behavioral variability can include not only variations of the same behavior, but also variability across qualitatively different behaviors driven by divergent cognitive states, such as fight-or-flight decisions. However, the neural circuit mechanisms that generate such divergent behaviors across trials are not well understood. To investigate this question, here we studied the visual-evoked responses of larval zebrafish to moving objects of various sizes, which we found exhibited highly variable and divergent responses across repetitions of the same stimulus. Given that the neuronal circuits underlying such behaviors span sensory, motor, and other brain areas, we built a novel Fourier light field microscope which enables high-resolution, whole-brain imaging of larval zebrafish during behavior. This enabled us to screen for neural loci which exhibited activity patterns correlated with behavioral variability. We found that despite the highly variable activity of single neurons, visual stimuli were robustly encoded at the population level, and the visual-encoding dimensions of neural activity did not explain behavioral variability. This robustness despite apparent single neuron variability was due to the multi-dimensional geometry of the neuronal population dynamics: almost all neural dimensions that were variable across individual trials, i.e. the "noise" modes, were orthogonal to those encoding for sensory information. Investigating this neuronal variability further, we identified two sparsely-distributed, brain-wide neuronal populations whose pre-motor activity predicted whether the larva would respond to a stimulus and, if so, which direction it would turn on a single-trial level. These populations predicted single-trial behavior seconds before stimulus onset, indicating they encoded time-varying internal modulating behavior, perhaps organizing behavior over longer timescales or enabling flexible behavior routines dependent on the animal's internal state. Our results provide the first whole-brain confirmation that sensory, motor, and internal variables are encoded in a highly mixed fashion throughout the brain and demonstrate that de-mixing each of these components at the neuronal population level is critical to understanding the mechanisms underlying the brain's remarkable flexibility and robustness.

Rapid, systematic updating of movement by accumulated decision evidence

Acting in the natural world requires not only deciding among multiple options but also converting decisions into motor commands. How the dynamics of decision formation influence the fine kinematics of response movement remains, however, poorly understood. Here we investigate how the accumulation of decision evidence shapes the response orienting trajectories in a task where freely-moving rats combine prior expectations and auditory information to select between two possible options. Response trajectories and their motor vigor are initially determined by the prior. Rats movements then incorporate sensory information as early as 60 ms after stimulus onset by accelerating or slowing depending on how much the stimulus supports their initial choice. When the stimulus evidence is in strong contradiction, rats change their mind and reverse their initial trajectory. Human subjects performing an equivalent task display a remarkably similar behavior. We encapsulate these results in a computational model that, by mapping the decision variable onto the movement kinematics at discrete time points, captures subjects' choices, trajectories and changes of mind. Our results show that motor responses are not ballistic. Instead, they are systematically and rapidly updated, as they smoothly unfold over time, by the parallel dynamics of the underlying decision process.

Dynamical constraints on neural population activity

The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.

Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex

We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.

Analysis of Fluency of Movement in Parkour Using a Video and Inertial Measurement Unit Technology

Fluency is a movement parameter combining smoothness and hesitation, and its objective measurement may be used to determine the effects of practice on sports performance. This study aimed to measure fluency in parkour, an acrobatic discipline comprising complex non-cyclical movements, which involves fluency as a critical aspect of performance. Inter-individual fluidity differences between advanced and novice athletes as well as intra-individual variations of fluency between different parts and subsequent repetitions of a path were addressed. Seventeen parkour participants were enrolled and divided into two groups based on their experience. We analysed signals captured from an inertial measurement unit fixed on the back of the pelvis of each participant during three consecutive repetitions of a specifically designed parkour routine under the guidance of video analysis. Two fluency parameters, namely smoothness and hesitation, were measured. Smoothness was calculated as the number of inflexions on the so-called jerk graph; hesitation was the percentage of the drop in the centre of mass velocity. Smoothness resulted in significantly lower values in advanced athletes (mean: 126.4; range: 36-192) than in beginners (mean: 179.37; range: 98-272) during one of the three motor activities (p = 0.02). A qualitative analysis of hesitation showed that beginner athletes tended to experience more prominent velocity drops and negative deflection than more advanced athletes. In conclusion, a system based on a video and an inertial measurement unit is a promising approach for quantification and the assessment of variability of fluency, and it is potentially beneficial to guide and evaluate the training process.

Muscular reflex gains reflect changes of mind in reaching

Decisions for action are accompanied by a continual processing of sensory information, sometimes resulting in a revision of the initial choice, called a change of mind (CoM). Although the motor system is tuned during the formation of a reach decision, it is unclear whether its preparatory state differs between CoM and non-CoM decisions. To test this, participants (n = 14) viewed a random-dot motion (RDM) stimulus of various coherence levels for a random viewing duration. At the onset of a mechanical perturbation that rapidly stretched the pectoralis muscle, they indicated the perceived motion direction by making a reaching movement to one of two targets. Using electromyography (EMG), we quantified the reflex gains of the pectoralis and posterior deltoid muscles. Results show that reflex gains scaled with both the coherence level and the viewing duration of the stimulus. We fit a drift diffusion model (DDM) to the behavioral choices. The decision variable (DV), derived from the DDM, correlated well with the measured reflex gain at the single-trial level. However, when matched on DV magnitude, reflex gains were significantly lower in CoM than non-CoM trials. We conclude that the internal state of the motor system, as measured by the spinal reflexes, reflects the continual deliberation on sensory evidence for action selection, including the postdecisional evidence that can lead to a change of mind.NEW & NOTEWORTHY Using behavioral findings, EMG, and computational modeling, we show that not only the perceptual decision outcome but also the accumulating evidence for that outcome is continuously sent to the relevant muscles. Moreover, we show that an upcoming change of mind can be detected in the motor periphery, suggesting that a correlate of the internal decision making process is being sent along.

Drifting population dynamics with transient resets characterize sensorimotor transformation in the monkey superior colliculus

To produce goal-directed eye movements known as saccades, we must channel sensory input from our environment through a process known as sensorimotor transformation. The behavioral output of this phenomenon (an accurate eye movement) is straightforward, but the coordinated activity of neurons underlying its dynamics is not well understood. We searched for a neural correlate of sensorimotor transformation in the activity patterns of simultaneously recorded neurons in the superior colliculus (SC) of three male rhesus monkeys performing a visually guided, delayed saccade task. Neurons in the intermediate layers produce a burst of spikes both following the appearance of a visual (sensory) stimulus and preceding an eye movement command, but many also exhibit a sustained activity level during the intervening time ("delay period"). This sustained activity could be representative of visual processing or motor preparation, along with countless cognitive processes. Using a novel measure we call the Visuomotor Proximity Index (VMPI), we pitted visual and motor signals against each other by measuring the degree to which each session's population activity (as summarized in a low-dimensional framework) could be considered more visual-like or more motor-like. The analysis highlighted two salient features of sensorimotor transformation. One, population activity on average drifted systematically toward a motor-like representation and intermittently reverted to a visual-like representation following a microsaccade. Two, activity patterns that drift to a stronger motor-like representation by the end of the delay period may enable a more rapid initiation of a saccade, substantiating the idea that this movement initiation mechanism is conserved across motor systems.

Parallel movement planning is achieved via an optimal preparatory state in motor cortex

How do patterns of neural activity in the motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that the motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyze preparatory activity in non-human primates planning not one but two movements simultaneously. As predicted by the theory, we find that parallel planning is achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a trade-off between the two movements. The theory quantitatively accounts for the relationship between this intermediate state and fluctuations in the animals' behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel and further point to motor planning as a controlled dynamical process.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

A robust role for motor cortex

The role of motor cortex in non-primate mammals remains unclear. More than a century of stimulation, anatomical and electrophysiological studies has implicated neural activity in this region with all kinds of movement. However, following the removal of motor cortex, rats retain most of their adaptive behaviors, including previously learned skilled movements. Here we revisit these two conflicting views of motor cortex and present a new behavior assay, challenging animals to respond to unexpected situations while navigating a dynamic obstacle course. Surprisingly, rats with motor cortical lesions show clear impairments facing an unexpected collapse of the obstacles, while showing no impairment with repeated trials in many motor and cognitive metrics of performance. We propose a new role for motor cortex: extending the robustness of sub-cortical movement systems, specifically to unexpected situations demanding rapid motor responses adapted to environmental context. The implications of this idea for current and future research are discussed.

Dimensionality reduction of calcium-imaged neuronal population activity

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a method to perform deconvolution and dimensionality reduction simultaneously (Calcium Imaging Linear Dynamical System, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

Functional properties of eyelid conditioned responses and involved brain centers

For almost a century the classical conditioning of nictitating membrane/eyelid responses has been used as an excellent and feasible experimental model to study how the brain organizes the acquisition, storage, and retrieval of new motor abilities in alert behaving mammals, including humans. Lesional, pharmacological, and electrophysiological approaches, and more recently, genetically manipulated animals have shown the involvement of numerous brain areas in this apparently simple example of associative learning. In this regard, the cerebellum (both cortex and nuclei) has received particular attention as a putative site for the acquisition and storage of eyelid conditioned responses, a proposal not fully accepted by all researchers. Indeed, the acquisition of this type of learning implies the activation of many neural processes dealing with the sensorimotor integration and the kinematics of the acquired ability, as well as with the attentional and cognitive aspects also involved in this process. Here, we address specifically the functional roles of three brain structures (red nucleus, cerebellar interpositus nucleus, and motor cortex) mainly involved in the acquisition and performance of eyelid conditioned responses and three other brain structures (hippocampus, medial prefrontal cortex, and claustrum) related to non-motor aspects of the acquisition process. The main conclusion is that the acquisition of this motor ability results from the contribution of many cortical and subcortical brain structures each one involved in specific (motor and cognitive) aspects of the learning process.

Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept

Team members co-regulate their activities and move together at the collective level of behavior while coordinating their actions toward shared goals. In parallel with team processes, team members need to resolve uncertainties arising from the changing task and environment. In this exploratory study we have measured the differential neurodynamics of seven two-person healthcare teams across time and brain regions during autonomous (taskwork) and collaborative (teamwork) segments of simulation training. The questions posed were: (1) whether these abstract and mostly integrated constructs could be separated neurodynamically; and, (2) what could be learned about taskwork and teamwork by trying to do so? The taskwork and teamwork frameworks used were Neurodynamic Information (NI), an electroencephalography (EEG) derived measure shown to be a neurodynamic proxy for the pauses and hesitations associated with individual uncertainty, and inter-brain EEG coherence (IBC) which is a required component of social interactions. No interdependency was observed between NI and IBC, and second-by-second dynamic comparisons suggested mutual exclusivity. These studies show that proxies for fundamental properties of teamwork and taskwork can be separated neurodynamically during team performances of ecologically valid tasks. The persistent expression of NI and IBC were not simultaneous suggesting that it may be difficult for team members to maintain inter-brain coherence while simultaneously reducing their individual uncertainties. Lastly, these separate dynamics occur over time frames of 15–30 s providing time for real-time detection and mitigation of individual and collaborative complications during training or live patient encounters.

Shared and specialized coding across posterior cortical areas for dynamic navigation decisions

Animals adaptively integrate sensation, planning, and action to navigate toward goal locations in ever-changing environments, but the functional organization of cortex supporting these processes remains unclear. We characterized encoding in approximately 90,000 neurons across the mouse posterior cortex during a virtual navigation task with rule switching. The encoding of task and behavioral variables was highly distributed across cortical areas but differed in magnitude, resulting in three spatial gradients for visual cue, spatial position plus dynamics of choice formation, and locomotion, with peaks respectively in visual, retrosplenial, and parietal cortices. Surprisingly, the conjunctive encoding of these variables in single neurons was similar throughout the posterior cortex, creating high-dimensional representations in all areas instead of revealing computations specialized for each area. We propose that, for guiding navigation decisions, the posterior cortex operates in parallel rather than hierarchically, and collectively generates a state representation of the behavior and environment, with each area specialized in handling distinct information modalities.

The role of dorsal premotor cortex in joint action stopping

Human sensorimotor interaction requires mutual behavioral adaptation as well as shared cognitive task representations (Joint Action, JA). Yet, an under-investigated aspect of JA is the neurobehavioral mechanisms employed to stop actions if the context calls for it. Sparse evidence points to the possible contribution of the left dorsal premotor cortex (lPMd) in sculpting movements according to the socio-interactive context. To clarify this issue, we ran two experiments integrating a classical stop signal paradigm with an ecological JA task. The first behavioral study shows longer Stop performance in the JA condition. In the second, we use transcranial magnetic stimulation to inhibit the lPMd or a control site (vertex). Results show that lPMd modulates the JA stopping performance. Action stopping is an important component of JA coordination, and here we provide evidence that lPMd is a key node of a brain network recruited for online mutual co-adaptation in social contexts.

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Interaction requires mutual adaptation and a shared cognitive task representation

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Sensorimotor representations must be negotiated between partners to achieve the goal

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Motor suppression mechanisms might be essential in Joint Action coordination

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Dorsal premotor cortex (PMd) plays a key role in guiding Joint Action coordination

Statistical neuroscience in the single trial limit

Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic 'noise' and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure - including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions - and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.

Bridging neuronal correlations and dimensionality reduction

Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.

Role of the motor cortex in the generation of classically conditioned eyelid and vibrissae responses

The eyelid motor system has been used for years as an experimental model for studying the neuronal mechanisms underlying motor and cognitive learning, mainly with classical conditioning procedures. Nonetheless, it is not known yet which brain structures, or neuronal mechanisms, are responsible for the acquisition, storage, and expression of these motor responses. Here, we studied the temporal correlation between unitary activities of identified eyelid and vibrissae motor cortex neurons and the electromyographic activity of the orbicularis oculi and vibrissae muscles and magnetically recorded eyelid positions during classical conditioning of eyelid and vibrissae responses, using both delay and trace conditioning paradigms in behaving mice. We also studied the involvement of motor cortex neurons in reflexively evoked eyelid responses and the kinematics and oscillatory properties of eyelid movements evoked by motor cortex microstimulation. Results show the involvement of the motor cortex in the performance of conditioned responses elicited during the classical conditioning task. However, a timing correlation analysis showed that both electromyographic activities preceded the firing of motor cortex neurons, which must therefore be related more with the reinforcement and/or proper performance of the conditioned responses than with their acquisition and storage.

Representational geometry of perceptual decisions in the monkey parietal cortex

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, representation and readout of the decision variables (DVs) are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Further, DVs are encoded as partially potentiated action plans through balance of activity of action-selective ensembles. Here, we test those core principles. We show that in a novel face-discrimination task, LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on context, indicating distinct optimal readouts for different tasks. We show similar manifolds in lateral and medial prefrontal cortices, suggesting similar representational geometry across decision-making circuits.

Modularity and robustness of frontal cortical networks

Neural activity underlying short-term memory is maintained by interconnected networks of brain regions. It remains unknown how brain regions interact to maintain persistent activity while exhibiting robustness to corrupt information in parts of the network. We simultaneously measured activity in large neuronal populations across mouse frontal hemispheres to probe interactions between brain regions. Activity across hemispheres was coordinated to maintain coherent short-term memory. Across mice, we uncovered individual variability in the organization of frontal cortical networks. A modular organization was required for the robustness of persistent activity to perturbations: each hemisphere retained persistent activity during perturbations of the other hemisphere, thus preventing local perturbations from spreading. A dynamic gating mechanism allowed hemispheres to coordinate coherent information while gating out corrupt information. Our results show that robust short-term memory is mediated by redundant modular representations across brain regions. Redundant modular representations naturally emerge in neural network models that learned robust dynamics.

Chimpanzees (Pan troglodytes) show subtle signs of uncertainty when choices are more difficult

Humans can tell when they find a task difficult. Subtle uncertainty behaviors like changes in motor speed and muscle tension precede and affect these experiences. Theories of animal metacognition likewise stress the importance of endogenous signals of uncertainty as cues that motivate metacognitive behaviors. However, while researchers have investigated second-order behaviors like information seeking and declining difficult trials in nonhuman animals, they have devoted little attention to the behaviors that express the cognitive conflict that gives rise to such behaviors in the first place. Here we explored whether three chimpanzees would, like humans, show hand wavering more when faced with more difficult choices in a touch screen transitive inference task. While accuracy was very high across all conditions, all chimpanzees wavered more frequently in trials that were objectively more difficult, demonstrating a signature behavior which accompanies experiences of difficulty in humans. This lends plausibility to the idea that feelings of uncertainty, like other emotions, can be studied in nonhuman animals. We propose to routinely assess uncertainty behaviors to inform models of procedural metacognition in nonhuman animals.

The Self-Prioritization Effect: Self-referential processing in movement highlights modulation at multiple stages

A wealth of recent research supports the validity of the Self-Prioritization Effect (SPE)—the performance advantage for responses to self-associated as compared with other-person-associated stimuli in a shape–label matching task. However, inconsistent findings have been reported regarding the particular stage(s) of information processing that are influenced. In one account, self-prioritization modulates multiple stages of processing, whereas according to a competing account, self-prioritization is driven solely by a modulation in central-stage information-processing. To decide between these two possibilities, the present study tested whether the self-advantage in arm movements previously reported could reflect a response bias using visual feedback (Experiment 1), or approach motivation processes (Experiments 1 and 2). In Experiment 1, visual feedback was occluded in a ballistic movement-time variant of the matching task, whereas in Experiment 2, task responses were directed away from the stimuli and the participant’s body. The advantage for self in arm-movement responses emerged in both experiments. The findings indicate that the self-advantage in arm-movement responses does not depend on the use of visual feedback or on a self/stimuli-directed response. They further indicate that self-relevance can modulate movement responses (predominantly) using proprioceptive, kinaesthetic, and tactile information. These findings support the view that self-relevance modulates arm-movement responses, countering the suggestion that self-prioritization only influences central-stage processes, and consistent with a multiple-stage influence instead.

Hematology and Culture Assessment of Cranially Implanted Rhesus Macaques (Macaca mulatta)

The use of percutaneous cranial implants in rhesus macaques (Macaca mulatta) has long been a valuable tool for neuroscience research. However, when treating and assessing these animals, veterinarians are required to make assumptions about diagnostic results due to a lack of research into how these implants affect physiology. Microbial cultures of cranial implant sites show an abundance of colonizing bacteria, but whether these microbes affect animal health and wellbeing is poorly understood. In addition, microbial antibiotic resistance can present significant health concerns for both the animals and the researchers. To help elucidate the relationship between percutaneous cranial implants and blood parameters, complete blood cell counts and serum chemistry results were assessed on 57 nonhuman primates at our institution from September 2001 to March 2017. Generalized estimating equations were used to compare the results before and after an animal's first implant surgery. This modelling showed that cranial implants were a significant predictor of alterations in the number of neutrophils, lymphocytes, and red blood cells, and in the concentration of hemoglobin, alkaline phosphatase, creatinine, calcium, phos- phorus, total protein, albumin, and globulin. Anaerobic and aerobic bacterial cultures were performed to identify bacteria associated with cranial implants. Staphylococcus spp., Streptococcus spp., and Corynebacterium spp. comprised the majority of the aerobic bacterial isolates, while Fusobacterium spp., Peptostreptococcus spp. and Bacterioides fragilis comprised the majority of anaerobic bacterial isolates. Using a Pearson r correlation for statistical analysis, we assessed whether any of these bacterial isolates developed antibiotic resistances over time. Cefazolin, the most frequently used antibiotic in monkeys in this study, was the only antimicrobial out of 41 agents tested to which bacteria developed resistance over time. These results indicate that percutaneous implants are associated with a generalized inflammatory state, multiple bacterial species are present at the implant site, and these bacteria may contribute to the inflammatory response.

A Hierarchical Attractor Network Model of perceptual versus intentional decision updates

Changes of Mind are a striking example of our ability to flexibly reverse decisions and change our own actions. Previous studies largely focused on Changes of Mind in decisions about perceptual information. Here we report reversals of decisions that require integrating multiple classes of information: 1) Perceptual evidence, 2) higher-order, voluntary intentions, and 3) motor costs. In an adapted version of the random-dot motion task, participants moved to a target that matched both the external (exogenous) evidence about dot-motion direction and a preceding internally-generated (endogenous) intention about which colour to paint the dots. Movement trajectories revealed whether and when participants changed their mind about the dot-motion direction, or additionally changed their mind about which colour to choose. Our results show that decision reversals about colour intentions are less frequent in participants with stronger intentions (Exp. 1) and when motor costs of intention pursuit are lower (Exp. 2). We further show that these findings can be explained by a hierarchical, multimodal Attractor Network Model that continuously integrates higher-order voluntary intentions with perceptual evidence and motor costs. Our model thus provides a unifying framework in which voluntary actions emerge from a dynamic combination of internal action tendencies and external environmental factors, each of which can be subject to Change of Mind.

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.

Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty

Neurodynamic organizations are information-based abstractions, expressed in bits, of the structure of long duration EEG amplitude levels. Neurodynamic information (NI, the variable of neurodynamic organization) is thought to continually accumulate as EEG amplitudes cycle through periods of persistent activation and deactivation in response to the activities and uncertainties of teamwork. Here we show that (1) Neurodynamic information levels were a better predictor of uncertainty and novice and expert behaviors than were the EEG power levels from which NI was derived. (2) Spatial and temporal parsing of team NI from experienced submarine navigation and healthcare teams showed that it was composed of discrete peaks with durations up to 20–60 s, and identified the involvement of activated delta waves when precise motor control was needed. (3) The relationship between NI and EEG power was complex varying by brain regions, EEG frequencies, and global vs. local brain interactions. The presence of an organizational system of information that parallels the amplitude of EEG rhythms is important as it provides a greatly reduced data dimension while retaining the essential system features, i.e., linkages to higher scale behaviors that span temporal and spatial scales of teamwork. In this way the combinatorial explosion of EEG rhythmic variables at micro levels become compressed into an intermediate system of information and organization which links to macro-scale team and team member behaviors. These studies provide an avenue for understanding how complex organizations arise from the dynamics of underlying micro-scale variables. The study also has practical implications for how micro-scale variables might be better represented, both conceptually and in terms of parsimony, for training machines to recognize human behaviors that span scales of teams.

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment¹. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject's upcoming decision². Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind³. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

Perceptual change-of-mind decisions are sensitive to absolute evidence magnitude

To navigate the world safely, we often need to rapidly 'change our mind' about decisions. Current models assume that initial decisions and change-of-mind decisions draw upon common sources of sensory evidence. In two-choice scenarios, this evidence may be 'relative' or 'absolute'. For example, when judging which of two objects is the brightest, the luminance difference and luminance ratio between the two objects are sources of 'relative' evidence, which are invariant across additive and multiplicative luminance changes. Conversely, the overall luminance of the two objects combined is a source of 'absolute' evidence, which necessarily varies across symmetric luminance manipulations. Previous studies have shown that initial decisions are sensitive to both relative and absolute evidence; however, it is unknown whether change-of-mind decisions are sensitive to absolute evidence. Here, we investigated this question across two experiments. In each experiment participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli remained on screen for a brief period and participants could change their response. To investigate the effect of absolute evidence, the overall luminance of the two squares was varied whilst either the luminance difference (Experiment 1) or luminance ratio (Experiment 2) was held constant. In both experiments we found that increases in absolute evidence led to faster, less accurate initial responses and slower changes of mind. Change-of-mind accuracy decreased when the luminance difference was held constant, but remained unchanged when the luminance ratio was fixed. We show that the three existing change-of-mind models cannot account for our findings. We then fit three alternative models, previously used to account for the effect of absolute evidence on one-off decisions, to the data. A leaky competing accumulator model best accounted for the changes in behaviour across absolute evidence conditions - suggesting an important role for input-dependent leak in explaining perceptual changes of mind.

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.

Eye movements as a readout of sensorimotor decision processes

Real-world tasks, such as avoiding obstacles, require a sequence of interdependent choices to reach accurate motor actions. Yet, most studies on primate decision making involve simple one-step choices. Here we analyze motor actions to investigate how sensorimotor decisions develop over time. In a go/no-go interception task human observers (n = 42) judged whether a briefly presented moving target would pass (interceptive hand movement required) or miss (no hand movement required) a strike box while their eye and hand movements were recorded. Go/no-go decision formation had to occur within the first few hundred milliseconds to allow time-critical interception. We found that the earliest time point at which eye movements started to differentiate actions (go versus no-go) preceded hand movement onset. Moreover, eye movements were related to different stages of decision making. Whereas higher eye velocity during smooth pursuit initiation was related to more accurate interception decisions (whether or not to act), faster pursuit maintenance was associated with more accurate timing decisions (when to act). These results indicate that pursuit initiation and maintenance are continuously linked to ongoing sensorimotor decision formation.NEW & NOTEWORTHY Here we show that eye movements are a continuous indicator of decision processes underlying go/no-go actions. We link different stages of decision formation to distinct oculomotor events during open- and closed-loop smooth pursuit. Critically, the earliest time point at which eye movements differentiate actions preceded hand movement onset, suggesting shared sensorimotor processing for eye and hand movements. These results emphasize the potential of studying eye movements as a readout of cognitive processes.

Harnessing behavioral diversity to understand neural computations for cognition

With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.

Decision-making in sensorimotor control

Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.

Distinct Sources of Variability Affect Eye Movement Preparation

The sequence of events leading to an eye movement to a target begins the moment visual information has reached the brain, well in advance of the eye movement itself. The process by which visual information is encoded and used to generate a motor plan has been the focus of substantial interest partly because of the rapid and reproducible nature of saccadic eye movements, and the key role that they play in primate behavior. Signals related to eye movements are present in much of the primate brain, yet most neurophysiological studies of the transition from vision to eye movements have measured the activity of one neuron at a time. Less is known about how the coordinated action of populations of neurons contribute to the initiation of eye movements. One cortical area of particular interest in this process is the frontal eye fields, a region of prefrontal cortex that has descending projections to oculomotor control centers. We recorded from populations of frontal eye field neurons in macaque monkeys engaged in a memory-guided saccade task. We found a variety of neurons with visually evoked responses, saccade-aligned responses, and mixtures of both. We took advantage of the simultaneous nature of the recordings to measure variability in individual neurons and pairs of neurons from trial-to-trial, as well as the moment-to-moment population activity structure. We found that these measures were related to saccadic reaction times, suggesting that the population-level organization of frontal eye field activity is important for the transition from perception to movement.SIGNIFICANCE STATEMENT The transition from perception to action involves coordination among neurons across the brain. In the case of eye movements, visual and motor signals coexist in individual neurons as well as in neighboring neurons. We used a task designed to compartmentalize the visual and motor aspects of this transition and studied populations of neurons in the frontal eye fields, a key cortical area containing neurons that are implicated in the transition from vision to eye movements. We found that the time required for subjects to produce an eye movement could be predicted from the statistics of the neuronal response of populations of frontal eye field neurons, suggesting that these neurons coordinate their activity to optimize the transition from perception to action.

Accurate Estimation of Neural Population Dynamics without Spike Sorting

A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind.

We make decisions with varying degrees of confidence and, if our confidence in a decision falls, we may change our mind. Here, the authors present a neuronal circuit model to account for how change of mind occurs under particular low-confidence conditions.

Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known

How deliberation on sensory cues and action selection interact in decision-related brain areas is still not well understood. Here, monkeys reached to one of two targets, whose colors alternated randomly between trials, by discriminating the dominant color of a checkerboard cue composed of different numbers of squares of the two target colors in different trials. In a Targets First task the colored targets appeared first, followed by the checkerboard; in a Checkerboard First task, this order was reversed. After both cues appeared in both tasks, responses of dorsal premotor cortex (PMd) units covaried with action choices, strength of evidence for action choices, and RTs- hallmarks of decision-related activity. However, very few units were modulated by checkerboard color composition or the color of the chosen target, even during the checkerboard deliberation epoch of the Checkerboard First task. These findings implicate PMd in the action-selection but not the perceptual components of the decision-making process in these tasks.

Cortical Areas Interact through a Communication Subspace

Most brain functions involve interactions among multiple, distinct areas or nuclei. For instance, visual processing in primates requires the appropriate relaying of signals across many distinct cortical areas. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Here we investigate how trial-to-trial fluctuations of population responses in primary visual cortex (V1) are related to simultaneously recorded population responses in area V2. Using dimensionality reduction methods, we find that V1-V2 interactions occur through a communication subspace: V2 fluctuations are related to a small subset of V1 population activity patterns, distinct from the largest fluctuations shared among neurons within V1. In contrast, interactions between subpopulations within V1 are less selective. We propose that the communication subspace may be a general, population-level mechanism by which activity can be selectively routed across brain areas.

Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.

Single Neuron Firing Rate Statistics in Motor Cortex During Execution and Observation of Movement

Mirror neurons, which fire during both the execution and observation of movement, are believed to play an important role in motor processing and learning. However, much work still remains to understand the similarities and differences in how these neurons compute in the motor cortex during movement execution and observation. Here, we performed experiments where a monkey both executes and observes a center-out-and-back task within the same experimental session. By recording from putatively the same neural population, we were able to analyze and compare single neuron statistics between movement execution and observation. We found that a majority of neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) have statistically different firing rate statistics between movement execution and observation. As a result of this difference, we then wondered if neurons during movement observation exhibited a similar characteristic to those during movement execution: changing of preferred directions as a function of movement speed. Interestingly, we found that while observed movement speed is encoded in the neural population, it only alters a small proportion of the neuron's firing rate statistics. These results suggest that neural populations in Ml and PMd process information related to movement differently between execution and observation.

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.