Rotational dynamics in motor cortex are consistent with a feedback controller

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

An Open Resource for Non-human Primate Optogenetics

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

Cortical Stimulation Induces Network-Wide Coherence Change In Non-Human Primate Somatosensory Cortex

Stimulation of the cortex can modulate the connectivity between brain regions. Although targeted neuroplasticity has been demonstrated in-vitro, in-vivo models have been inconsistent in their response to stimulation. In this paper, we tested various stimulation protocols to characterize the effect of stimulation on coherence in the non-human primate cortex in-vivo. We found that the stimulation latency, the state of the cortex during stimulation, and the stimulation site all affected the modulation of cortical coherence. We further investigated features of a resting-state network that could predict how a connection is likely to change with stimulation.

Novel techniques for large-scale manipulations of cortical networks in non-human primates

Optogenetics is a powerful tool that enables millisecond-level control of the activity of specific groups of neurons. Furthermore, it has the great advantage of artifact free recordings. These characteristics make this technique ideal for relating brain function to behavior in animals with great behavioral capabilities such as non-human primates (NHPs). We recently introduced a practical, stable interface for optogenetic stimulation and recording of large-scale cortical circuits in NHPs. Here we present the various potentials of this interface for studying circuits and connectivity at a large-scale and for relating it to behavior.

Convection Enhanced Delivery of Optogenetic Adeno-associated Viral Vector to the Cortex of Rhesus Macaque Under Guidance of Online MRI Images

In non-human primate (NHP) optogenetics, infecting large cortical areas with viral vectors is often a difficult and time-consuming task. Here, we demonstrate the use of magnetic resonance (MR)-guided convection enhanced delivery (CED) of optogenetic viral vectors into primary somatosensory (S1) and motor (M1) cortices of macaques to obtain efficient, widespread cortical expression of light-sensitive ion channels. Adeno-associated viral (AAV) vectors encoding the red-shifted opsin C1V1 fused to yellow fluorescent protein (EYFP) were injected into the cortex of rhesus macaques under MR-guided CED. Three months post-infusion, epifluorescent imaging confirmed large regions of optogenetic expression (>130 mm²) in M1 and S1 in two macaques. Furthermore, we were able to record reliable light-evoked electrophysiology responses from the expressing areas using micro-electrocorticographic arrays. Later histological analysis and immunostaining against the reporter revealed widespread and dense optogenetic expression in M1 and S1 corresponding to the distribution indicated by epifluorescent imaging. This technique enables us to obtain expression across large areas of the cortex within a shorter period of time with minimal damage compared to the traditional techniques and can be an optimal approach for optogenetic viral delivery in large animals such as NHPs. This approach demonstrates great potential for network-level manipulation of neural circuits with cell-type specificity in animal models evolutionarily close to humans.

Targeted cortical reorganization using optogenetics in non-human primates

Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex (Yazdan-Shahmorad et al., 2016). We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation.

From riding a bike to reaching for a cup of coffee, all skilled actions rely on precise connections between the sensory and motor areas of the brain. While sensory areas receive and analyse input from the senses, motor areas plan and trigger muscle contractions. Precisely adjusting the connections between these and other areas enables us to learn new skills, and it also helps us to relearn skills lost as a result of brain injury or stroke.

About 70 years ago, a psychologist named Donald Hebb came up with an idea for how this process might occur. He proposed that whenever two neurons are active at the same time, the connection between them becomes stronger. This idea, that ‘cells that fire together, wire together’, became known as Hebb’s rule. Many studies have since shown that Hebb’s rule can explain changes in the strength of connections between pairs of neurons. But can it also explain how connections between entire brain regions become stronger or weaker?

New results show that it can. The data were obtained using a technique called optogenetics, in which viruses are used to introduce genes for light-sensitive proteins into neurons. Shining light onto the brain will then activate any cells within that area that contain the resulting proteins. Yazdan-Shahmorad, Silversmith et al. used this technique to activate small regions of either sensory or motor brain tissue in live macaque monkeys. Doing so strengthened the overall connectivity between the two areas. The effects were more variable at the level of smaller brain regions, with some connections becoming weaker rather than stronger. However, Yazdan-Shahmorad, Silversmith et al. show that Hebb’s rule explains most of the observed changes.

Many neurological and psychiatric disorders stem from abnormal brain connectivity. Simple forms of brain stimulation are already used to treat certain neurological disorders, such as Parkinson’s disease. Stimulating the brain to induce specific changes in connectivity may ultimately enable us to leverage the brain’s natural learning mechanisms to cure, instead of just treat, these conditions.

A silicon carbide array for electrocorticography and peripheral nerve recording

OBJECTIVE

Current neural probes have a limited device lifetime of a few years. Their common failure mode is the degradation of insulating films and/or the delamination of the conductor-insulator interfaces. We sought to develop a technology that does not suffer from such limitations and would be suitable for chronic applications with very long device lifetimes.

APPROACH

We developed a fabrication method that integrates polycrystalline conductive silicon carbide with insulating silicon carbide. The technology employs amorphous silicon carbide as the insulator and conductive silicon carbide at the recording sites, resulting in a seamless transition between doped and amorphous regions of the same material, eliminating heterogeneous interfaces prone to delamination. Silicon carbide has outstanding chemical stability, is biocompatible, is an excellent molecular barrier and is compatible with standard microfabrication processes.

MAIN RESULTS

We have fabricated silicon carbide electrode arrays using our novel fabrication method. We conducted in vivo experiments in which electrocorticography recordings from the primary visual cortex of a rat were obtained and were of similar quality to those of polymer based electrocorticography arrays. The silicon carbide electrode arrays were also used as a cuff electrode wrapped around the sciatic nerve of a rat to record the nerve response to electrical stimulation. Finally, we demonstrated the outstanding long term stability of our insulating silicon carbide films through accelerated aging tests.

SIGNIFICANCE

Clinical translation in neural engineering has been slowed in part due to the poor long term performance of current probes. Silicon carbide devices are a promising technology that may accelerate this transition by enabling truly chronic applications.

Encoding and Decoding of Multi-Channel ICMS in Macaque Somatosensory Cortex

Naturalistic control of brain-machine interfaces will require artificial proprioception, potentially delivered via intracortical microstimulation (ICMS). We have previously shown that multi-channel ICMS can guide a monkey reaching to unseen targets in a planar workspace. Here, we expand on that work, asking how ICMS is decoded into target angle and distance by analyzing the performance of a monkey when ICMS feedback was degraded. From the resulting pattern of errors, we found that the animal's estimate of target direction was consistent with a weighted circular-mean strategy-close to the optimal decoding strategy given the ICMS encoding. These results support our previous finding that animals can learn to use this artificial sensory feedback in an efficient and naturalistic manner.

Learning to Estimate Dynamical State with Probabilistic Population Codes

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

A basic task for animals is to track objects—predators, prey, even their own limbs—as they move through the world. Because the position estimates provided by the senses are not error-free, higher levels of performance can be, and are, achieved when the velocity and acceleration, as well as the position, of the object are taken into account. Likewise, tracking of limbs under voluntary control can be improved by considering the motor command that is (partially) responsible for its trajectory. Engineers have built tools to solve precisely these problems, and even to learn dynamical features of the object to be tracked. How does the brain do it? We show how artificial networks of neurons can learn to solve this task, simply by trying to become good predictive models of their incoming data—as long as some of those data are the activities of the neurons themselves at a fixed time delay, while the remainder (imperfectly) report the current position. The tracking scheme the network learns to use—keeping track of past positions; the corresponding receptive fields; and the manner in which they are learned, provide predictions for brain areas involved in tracking, like the posterior parietal cortex.

Strategies for optical control and simultaneous electrical readout of extended cortical circuits

BACKGROUND

To dissect the intricate workings of neural circuits, it is essential to gain precise control over subsets of neurons while retaining the ability to monitor larger-scale circuit dynamics. This requires the ability to both evoke and record neural activity simultaneously with high spatial and temporal resolution.

NEW METHOD

In this paper we present approaches that address this need by combining micro-electrocorticography (μECoG) with optogenetics in ways that avoid photovoltaic artifacts.

RESULTS

We demonstrate that variations of this approach are broadly applicable across three commonly studied mammalian species - mouse, rat, and macaque monkey - and that the recorded μECoG signal shows complex spectral and spatio-temporal patterns in response to optical stimulation.

COMPARISON WITH EXISTING METHODS

While optogenetics provides the ability to excite or inhibit neural subpopulations in a targeted fashion, large-scale recording of resulting neural activity remains challenging. Recent advances in optical physiology, such as genetically encoded Ca(2+) indicators, are promising but currently do not allow simultaneous recordings from extended cortical areas due to limitations in optical imaging hardware.

CONCLUSIONS

We demonstrate techniques for the large-scale simultaneous interrogation of cortical circuits in three commonly used mammalian species.

Learning multisensory integration and coordinate transformation via density estimation

Sensory processing in the brain includes three key operations: multisensory integration-the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations-the change of reference frame for a stimulus (e.g., retinotopic to body-centered) effected through knowledge about an intervening variable (e.g., gaze position); and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned-but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations.

Sensory integration for reaching: models of optimality in the context of behavior and the underlying neural circuits

Although multisensory integration has been well modeled at the behavioral level, the link between these behavioral models and the underlying neural circuits is still not clear. This gap is even greater for the problem of sensory integration during movement planning and execution. The difficulty lies in applying simple models of sensory integration to the complex computations that are required for movement control and to the large networks of brain areas that perform these computations. Here I review psychophysical, computational, and physiological work on multisensory integration during movement planning, with an emphasis on goal-directed reaching. I argue that sensory transformations must play a central role in any modeling effort. In particular, the statistical properties of these transformations factor heavily into the way in which downstream signals are combined. As a result, our models of optimal integration are only expected to apply "locally," that is, independently for each brain area. I suggest that local optimality can be reconciled with globally optimal behavior if one views the collection of parietal sensorimotor areas not as a set of task-specific domains, but rather as a palette of complex, sensorimotor representations that are flexibly combined to drive downstream activity and behavior.

Sensory transformations and the use of multiple reference frames for reach planning

The sensory signals that drive movement planning arrive in a variety of “reference frames”, so integrating or comparing them requires sensory transformations. We propose a model where the statistical properties of sensory signals and their transformations determine how these signals are used. This model captures the patterns of gaze-dependent errors found in our human psychophysics experiment when the sensory signals available for reach planning are varied. These results challenge two widely held ideas: error patterns directly reflect the reference frame of the underlying neural representation, and it is preferable to use a single common reference frame for movement planning. We show that gaze-dependent error patterns, often cited as evidence for retinotopic reach planning, can be explained by a transformation bias and are not exclusively linked to retinotopic representations. Further, the presence of multiple reference frames allows for optimal use of available sensory information and explains task-dependent reweighting of sensory signals.

Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics

The sensorimotor calibration of visually guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for > or =20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a "state noise" process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments.

Modeling sensorimotor learning with linear dynamical systems

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation-the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a substantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.

Modeling Sensorimotor Learning with Linear Dynamical Systems

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation— the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a sub-stantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.

Flexible strategies for sensory integration during motor planning

When planning target-directed reaching movements, human subjects combine visual and proprioceptive feedback to form two estimates of the arm's position: one to plan the reach direction, and another to convert that direction into a motor command. These position estimates are based on the same sensory signals but rely on different combinations of visual and proprioceptive input, suggesting that the brain weights sensory inputs differently depending on the computation being performed. Here we show that the relative weighting of vision and proprioception depends both on the sensory modality of the target and on the information content of the visual feedback, and that these factors affect the two stages of planning independently. The observed diversity of weightings demonstrates the flexibility of sensory integration and suggests a unifying principle by which the brain chooses sensory inputs so as to minimize errors arising from the transformation of sensory signals between coordinate frames.

Multisensory integration during motor planning

When planning goal-directed reaches, subjects must estimate the position of the arm by integrating visual and proprioceptive signals from the sensory periphery. These integrated position estimates are required at two stages of motor planning: first to determine the desired movement vector, and second to transform the movement vector into a joint-based motor command. We quantified the contributions of each sensory modality to the position estimate formed at each planning stage. Subjects made reaches in a virtual reality environment in which vision and proprioception were dissociated by shifting the location of visual feedback. The relative weighting of vision and proprioception at each stage was then determined using computational models of feedforward motor control. We found that the position estimate used for movement vector planning relies mostly on visual input, whereas the estimate used to compute the joint-based motor command relies more on proprioceptive signals. This suggests that when estimating the position of the arm, the brain selects different combinations of sensory input based on the computation in which the resulting estimate will be used.

Parietal representation of object-based saccades

When monkeys make saccadic eye movements to simple visual targets, neurons in the lateral intraparietal area (LIP) display a retinotopic, or eye-centered, coding of the target location. However natural saccadic eye movements are often directed at objects or parts of objects in the visual scene. In this paper we investigate whether LIP represents saccadic eye movements differently when the target is specified as part of a visually displayed object. Monkeys were trained to perform an object-based saccade task that required them to make saccades to previously cued parts of an abstract object after the object reappeared in a new orientation. We recorded single neurons in area LIP of two macaque monkeys and analyzed their activity in the object-based saccade task, as well as two control tasks: a standard memory saccade task and a fixation task with passive object viewing. The majority of LIP neurons that were tuned in the memory saccade task were also tuned in the object-based saccade task. Using a hierarchical generalized linear model analysis, we compared the effects of three different spatial variables on the firing rate: the retinotopic location of the target, the object-fixed location of the target, and the orientation of the object in space. There was no evidence of an explicit object-fixed representation in the activity in LIP during either of the object-based tasks. In other words, no cells had receptive fields that rotated with the object. While some cells showed a modulation of activity due to the location of the target on the object, these variations were small compared to the retinotopic effects. For most cells, firing rates were best accounted for by either the retinotopic direction of the movement, the orientation of the object, or both spatial variables. The preferred direction of these retinotopic and object orientation effects were found to be invariant across tasks. On average, the object orientation effects were consistent with the retinotopic coding of potential target locations on the object. This interpretation is supported by the fact that the magnitude of these two effects were roughly equal in the early portions of the trial, but around the time of the motor response, the retinotopic effects dominated. We conclude that LIP uses the same retinotopic coding of saccade target whether the target is specified as an absolute point in space or as a location on a moving object.

The planning and control of reaching movements

The notion of internal models has become central to the study of visually guided reaching. Armed with this theoretical framework, researchers are gleaning insights into long-standing problems in the field, such as the ability to respond rapidly to changes in the location of a reach target and the fine control of the multi-joint dynamics of the arm. A key factor in these advances is our increased understanding of how the brain integrates feedforward control signals, sensory feedback, and predictions based on internal models of the arm.

The role of inertial sensitivity in motor planning

To achieve a given motor task a single trajectory must be chosen from the infinite set of possibilities consistent with the task. To investigate such motor planning in a natural environment, we examined the kinematics of reaching movements made around a visual obstacle in three-dimensional space. Within each session, the start and end points of the movement were uniformly varied around the obstacle. However, the distribution of the near points, where the paths came closest to the obstacle, showed a strong anisotropy, clustering at the poles of a preferred axis through the center of the obstacle. The preferred axes for movements made with the left and right arms were mirror symmetric about the midsagittal plane, suggesting that the anisotropy stems from intrinsic properties of the arm rather than extrinsic visual factors. One account of these results is a sensitivity model of motor planning, in which the movement path is skewed so that when the hand passes closest to the obstacle, the arm is in a configuration that is least sensitive to perturbations that might cause collision. To test this idea, we measured the mobility ellipse of the arm. The mobility minor axis represents the direction in which the hand is most inertially stable to a force perturbation. In agreement with the sensitivity model, the mobility minor axis was not significantly different from the preferred near point axis. The results suggest that the sensitivity of the arm to perturbations, as determined by its inertial stability, is taken into account in the planning process.

Obstacle avoidance and a perturbation sensitivity model for motor planning

A novel obstacle avoidance paradigm was used to investigate the planning of human reaching movements. We explored whether the CNS plans arm movements based entirely on the visual space kinematics of the movements, or whether the planning process incorporates specific details of the biomechanical plant to optimize the trajectory plan. Participants reached around an obstacle, the tip of which remained fixed in space throughout the experiment. When the obstacle and the start and target locations were rotated about the tip of the obstacle, the visually specified task constraints retained a rotational symmetry. If movements are planned in visual space, as indicated from a variety of studies on planar point-to-point movements, the resulting trajectories should also be rotationally symmetric across trials. However, systematic variations in movement path were observed as the orientation of the obstacle was changed. These path asymmetries can be accounted for by a class of models in which the planner reduces the likelihood of collision with the obstacle by taking into account the anisotropic sensitivity of the arm to external perturbations or uncertainty in joint level control or proprioception. The model that best matches the experimental results uses planning criteria based on the inertial properties of the arm.