Autonomous head-mounted electrophysiology systems for freely behaving primates

Tasks and their role in visual neuroscience

Vision is widely used as a model system to gain insights into how sensory inputs are processed and interpreted by the brain. Historically, careful quantification and control of visual stimuli have served as the backbone of visual neuroscience. There has been less emphasis, however, on how an observer's task influences the processing of sensory inputs. Motivated by diverse observations of task-dependent activity in the visual system, we propose a framework for thinking about tasks, their role in sensory processing, and how we might formally incorporate tasks into our models of vision.

Optrode recording of an entorhinal-cortical circuit in freely moving mice

The deep layers of medial entorhinal cortex (MEC) are considered a crucial station for spatial cognition and memory. The deep sublayer Va of MEC (MECVa) serves as the output stage of the entorhinal-hippocampal system and sends extensive projections to brain cortical areas. However, the functional heterogeneity of these efferent neurons in MECVa is poorly understood, due to the difficulty of performing single-neuron activity recording from the narrow band of cell population while the animals are behaving. In the current study, we combined multi-electrode electrophysiological recording and optical stimulation to record cortical-projecting MECVa neurons at single-neuron resolution in freely moving mice. First, injection of a viral Cre-LoxP system was used to express channelrhodopsin-2 specifically in MECVa neurons that project to the medial part of the secondary visual cortex (V2M-projecting MECVa neurons). Then, a lightweight, self-made optrode was implanted into MECVa to identify the V2M-projecting MECVa neurons and to enable single-neuron activity recordings in mice performing the open field test and 8-arm radial maze. Our results demonstrate that optrode approach is an accessible and reliable method for single-neuron recording of V2M-projecting MECVa neurons in freely moving mice, paving the way for future circuit studies designed to characterize the activity of MECVa neurons during specific tasks.

Head-mounted optical imaging and optogenetic stimulation system for use in behaving primates

Advances in optical technology have revolutionized studies of brain function in freely behaving mice. Here, we describe an optical imaging and stimulation device for use in primates that easily attaches to an intracranial chamber. It consists of affordable commercially available or 3D-printed components: a monochromatic camera, a small standard lens, a wireless μLED stimulator powered by an induction coil, and an LED array for illumination. We show that the intrinsic imaging performance of this device is comparable to a standard benchtop system in revealing the functional organization of the visual cortex for awake macaques in a primate chair or under anesthesia. Imaging revealed neural modulatory effects of wireless focal optogenetic stimulation aimed at identified functional domains. With a 1 to 2 cm field of view, 100× larger than previously used in primates without head restraint, our device permits widefield optical imaging and optogenetic stimulation for ethological studies in primates.

A markerless platform for ambulatory systems neuroscience

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque

Microendoscopic calcium imaging with one-photon miniature microscopes enables unprecedented readout of neural circuit dynamics during active behavior in rodents. In this study, we describe successful application of this technology in the rhesus macaque, demonstrating plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from large populations of neurons simultaneously in bilateral dorsal premotor cortices during performance of a naturalistic motor reach task. Imaging is stable over several months, allowing us to longitudinally track individual neurons and monitor their relationship to motor behavior over time. We observe neuronal calcium dynamics selective for reach direction, which we could use to decode the animal's trial-by-trial motor behavior. This work establishes head-mounted microendoscopic calcium imaging in macaques as a powerful approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and it promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease.

Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex

System neuroscience of motor cognition regarding the space beyond immediate reach mandates free, yet experimentally controlled movements. We present an experimental environment (Reach Cage) and a versatile visuo-haptic interaction system (MaCaQuE) for investigating goal-directed whole-body movements of unrestrained monkeys. Two rhesus monkeys conducted instructed walk-and-reach movements towards targets flexibly positioned in the cage. We tracked 3D multi-joint arm and head movements using markerless motion capture. Movements show small trial-to-trial variability despite being unrestrained. We wirelessly recorded 192 broad-band neural signals from three cortical sensorimotor areas simultaneously. Single unit activity is selective for different reach and walk-and-reach movements. Walk-and-reach targets could be decoded from premotor and parietal but not motor cortical activity during movement planning. The Reach Cage allows systems-level sensorimotor neuroscience studies with full-body movements in a configurable 3D spatial setting with unrestrained monkeys. We conclude that the primate frontoparietal network encodes reach goals beyond immediate reach during movement planning.

The brain during free movement - What can we learn from the animal model

Animals, just like humans, can freely move. They do so for various important reasons, such as finding food and escaping predators. Observing these behaviors can inform us about the underlying cognitive processes. In addition, while humans can convey complicated information easily through speaking, animals need to move their bodies to communicate. This has prompted many creative solutions by animal neuroscientists to enable studying the brain during movement. In this review, we first summarize how animal researchers record from the brain while an animal is moving, by describing the most common neural recording techniques in animals and how they were adapted to record during movement. We further discuss the challenge of controlling or monitoring sensory input during free movement. However, not only is free movement a necessity to reflect the outcome of certain internal cognitive processes in animals, it is also a fascinating field of research since certain crucial behavioral patterns can only be observed and studied during free movement. Therefore, in a second part of the review, we focus on some key findings in animal research that specifically address the interaction between free movement and brain activity. First, focusing on walking as a fundamental form of free movement, we discuss how important such intentional movements are for understanding processes as diverse as spatial navigation, active sensing, and complex motor planning. Second, we propose the idea of regarding free movement as the expression of a behavioral state. This view can help to understand the general influence of movement on brain function. Together, the technological advancements towards recording from the brain during movement, and the scientific questions asked about the brain engaged in movement, make animal research highly valuable to research into the human "moving brain".

A Markerless 3D Computerized Motion Capture System Incorporating a Skeleton Model for Monkeys

In this study, we propose a novel markerless motion capture system (MCS) for monkeys, in which 3D surface images of monkeys were reconstructed by integrating data from four depth cameras, and a skeleton model of the monkey was fitted onto 3D images of monkeys in each frame of the video. To validate the MCS, first, estimated 3D positions of body parts were compared between the 3D MCS-assisted estimation and manual estimation based on visual inspection when a monkey performed a shuttling behavior in which it had to avoid obstacles in various positions. The mean estimation error of the positions of body parts (3-14 cm) and of head rotation (35-43°) between the 3D MCS-assisted and manual estimation were comparable to the errors between two different experimenters performing manual estimation. Furthermore, the MCS could identify specific monkey actions, and there was no false positive nor false negative detection of actions compared with those in manual estimation. Second, to check the reproducibility of MCS-assisted estimation, the same analyses of the above experiments were repeated by a different user. The estimation errors of positions of most body parts between the two experimenters were significantly smaller in the MCS-assisted estimation than in the manual estimation. Third, effects of methamphetamine (MAP) administration on the spontaneous behaviors of four monkeys were analyzed using the MCS. MAP significantly increased head movements, tended to decrease locomotion speed, and had no significant effect on total path length. The results were comparable to previous human clinical data. Furthermore, estimated data following MAP injection (total path length, walking speed, and speed of head rotation) correlated significantly between the two experimenters in the MCS-assisted estimation (r = 0.863 to 0.999). The results suggest that the presented MCS in monkeys is useful in investigating neural mechanisms underlying various psychiatric disorders and developing pharmacological interventions.

The Extended Mirror Neuron Network

Mirror neurons (MNs) are a fascinating class of cells originally discovered in the ventral premotor cortex (PMv) and, subsequently, in the inferior parietal lobule (IPL) of the macaque, which become active during both the execution and observation of actions. In this review, I will first highlight the mounting evidence indicating that mirroring others’ actions engages a broad system of reciprocally connected cortical areas, which extends well beyond the classical IPL-PMv circuit and might even include subcortical regions such as the basal ganglia. Then, I will present the most recent findings supporting the idea that the observation of one’s own actions, which might play a role in the ontogenetic origin and tuning of MNs, retains a particular relevance within the adult MN system. Finally, I will propose that both cortical and subcortical mechanisms do exist to decouple MN activity from the motor output, in order to render it exploitable for high-order perceptual, cognitive, and even social functions. The findings reviewed here provide an original framework for envisaging the main challenges and experimental directions of future neurophysiological and neuroanatomical studies of the monkey MN system.

An optimized surgical approach for obtaining stable extracellular single-unit recordings from the cerebellum of head-fixed behaving mice

BACKGROUND

Electrophysiological recording approaches are essential for understanding brain function. Among these approaches are various methods of performing single-unit recordings. However, a major hurdle to overcome when recording single units in vivo is stability. Poor stability results in a low signal-to-noise ratio, which makes it challenging to isolate neuronal signals. Proper isolation is needed for differentiating a signal from neighboring cells or the noise inherent to electrophysiology. Insufficient isolation makes it impossible to analyze full action potential waveforms. A common source of instability is an inadequate surgery. Problems during surgery cause blood loss, tissue damage and poor healing of the surrounding tissue, limited access to the target brain region, and, importantly, unreliable fixation points for holding the mouse's head.

NEW METHOD

We describe an optimized surgical procedure that ensures limited tissue damage and delineate a method for implanting head plates to hold the animal firmly in place.

RESULTS

Using the cerebellum as a model, we implement an extracellular recording technique to acquire single units from Purkinje cells and cerebellar nuclear neurons in behaving mice. We validate the stability of our method by holding single units after injecting the powerful tremorgenic drug harmaline. We performed multiple structural analyses after recording.

COMPARISON WITH EXISTING METHODS

Our approach is ideal for studying neuronal function in active mice and valuable for recording single-neuron activity when considerable motion is unavoidable.

CONCLUSIONS

The surgical principles we present for accessing the cerebellum can be easily adapted to examine the function of neurons in other brain regions.

A wireless transmission neural interface system for unconstrained non-human primates

OBJECTIVE

Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications.

APPROACH

To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation.

MAIN RESULTS

We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm.

SIGNIFICANCE

We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish

In vivo functional imaging at single-neuron resolution is an important approach to visualize biological processes in neuroscience. Light sheet microscopy (LSM) is a cutting edge in vivo imaging technique that provides micron-scale spatial resolution at high frame rate. Due to the scattering and absorption of tissue, however, conventional LSM is inadequate to resolve cells because of the attenuated signal to noise ratio (SNR). Using dual-beam illumination and confocal dual-slit detection, here a dual-slit confocal LSM is demonstrated to obtain the SNR enhanced images with frame rate twice as high as line confocal LSM method. Through theoretical calculations and experiments, the correlation between the slit's width and SNR was determined to optimize the image quality. In vivo whole brain structural imaging stacks and the functional imaging sequences of single slice were obtained for analysis of calcium activities at single-cell resolution. A two-fold increase in imaging speed of conventional confocal LSM makes it possible to capture the sequence of the neurons' activities and help reveal the potential functional connections in the whole zebrafish's brain.

Intention estimation in brain-machine interfaces

OBJECTIVE

The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF).

APPROACH

This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm.

MAIN RESULTS

Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied.

SIGNIFICANCE

These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.

A freely-moving monkey treadmill model

OBJECTIVE

Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement.

APPROACH

We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill.

MAIN RESULTS

Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions.

SIGNIFICANCE

Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

Cortical control of arm movements: a dynamical systems perspective

Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

OBJECTIVE

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex.

APPROACH

One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs).

MAIN RESULTS

Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques.

SIGNIFICANCE

These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Neural population dynamics during reaching

Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.

Monkey models for brain-machine interfaces: the need for maintaining diversity

Brain-machine interfaces (BMIs) aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic arms, computer cursors, and other assistive devices. Animal models are central to the development of these systems and have helped enable the successful translation of the first generation of BMIs. As we move toward next-generation systems, we face the question of which animal models will aid broader patient populations and achieve even higher performance, robustness, and functionality. We review here four general types of rhesus monkey models employed in BMI research, and describe two additional, complementary models. Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease.

Simbios: an NIH national center for physics-based simulation of biological structures

Physics-based simulation provides a powerful framework for understanding biological form and function. Simulations can be used by biologists to study macromolecular assemblies and by clinicians to design treatments for diseases. Simulations help biomedical researchers understand the physical constraints on biological systems as they engineer novel drugs, synthetic tissues, medical devices, and surgical interventions. Although individual biomedical investigators make outstanding contributions to physics-based simulation, the field has been fragmented. Applications are typically limited to a single physical scale, and individual investigators usually must create their own software. These conditions created a major barrier to advancing simulation capabilities. In 2004, we established a National Center for Physics-Based Simulation of Biological Structures (Simbios) to help integrate the field and accelerate biomedical research. In 6 years, Simbios has become a vibrant national center, with collaborators in 16 states and eight countries. Simbios focuses on problems at both the molecular scale and the organismal level, with a long-term goal of uniting these in accurate multiscale simulations.

Wireless multi-channel single unit recording in freely moving and vocalizing primates

The ability to record well-isolated action potentials from individual neurons in naturally behaving animals is crucial for understanding neural mechanisms underlying natural behaviors. Traditional neurophysiology techniques, however, require the animal to be restrained which often restricts natural behavior. An example is the common marmoset (Callithrix jacchus), a highly vocal New World primate species, used in our laboratory to study the neural correlates of vocal production and sensory feedback. When restrained by traditional neurophysiological techniques marmoset vocal behavior is severely inhibited. Tethered recording systems, while proven effective in rodents pose limitations in arboreal animals such as the marmoset that typically roam in a three-dimensional environment. To overcome these obstacles, we have developed a wireless neural recording technique that is capable of collecting single-unit data from chronically implanted multi-electrodes in freely moving marmosets. A lightweight, low power and low noise wireless transmitter (headstage) is attached to a multi-electrode array placed in the premotor cortex of the marmoset. The wireless headstage is capable of transmitting 15 channels of neural data with signal-to-noise ratio (SNR) comparable to a tethered system. To minimize radio-frequency (RF) and electro-magnetic interference (EMI), the experiments were conducted within a custom designed RF/EMI and acoustically shielded chamber. The individual electrodes of the multi-electrode array were periodically advanced to densely sample the cortical layers. We recorded single-unit data over a period of several months from the frontal cortex of two marmosets. These recordings demonstrate the feasibility of using our wireless recording method to study single neuron activity in freely roaming primates.

Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

The influence of experimental manipulations on chewing speed during in vivo laboratory research in tufted capuchins (Cebus apella)

Even though in vivo studies of mastication in living primates are often used to test functional and adaptive hypotheses explaining primate masticatory behavior, we currently have little data addressing how experimental procedures performed in the laboratory influence mastication. The obvious logistical issue in assessing how animal manipulation impacts feeding physiology reflects the difficulty in quantifying mechanical parameters without handling the animal. In this study, we measured chewing cycle duration as a mechanical variable that can be collected remotely to: 1) assess how experimental manipulations affect chewing speed in Cebus apella, 2) compare captive chewing cycle durations to that of wild conspecifics, and 3) document sources of variation (beyond experimental manipulation) impacting captive chewing cycle durations. We find that experimental manipulations do increase chewing cycle durations in C. apella by as much as 152 milliseconds (ms) on average. These slower chewing speeds are mainly an effect of anesthesia (and/or restraint), rather than electrode implantation or more invasive surgical procedures. Comparison of captive and wild C. apella suggest there is no novel effect of captivity on chewing speed, although this cannot unequivocally demonstrate that masticatory mechanics are similar in captive and wild individuals. Furthermore, we document significant differences in cycle durations due to inter-individual variation and food type, although duration did not always significantly correlate with mechanical properties of foods. We advocate that the significant reduction in chewing speed be considered as an appropriate qualification when applying the results of laboratory-based feeding studies to adaptive explanations of primate feeding behaviors.

Combining Wireless Neural Recording and Video Capture for the Analysis of Natural Gait

Neural control of movement is typically studied in constrained environments where there is a reduced set of possible behaviors. This constraint may unintentionally limit the applicability of findings to the generalized case of unconstrained behavior. We hypothesize that examining the unconstrained state across multiple behavioral contexts will lead to new insights into the neural control of movement and help advance the design of neural prosthetic decode algorithms. However, to pursue electrophysiological studies in such a manner requires a more flexible framework for experimentation. We propose that head-mounted neural recording systems with wireless data transmission, combined with markerless computer-vision based motion tracking, will enable new, less constrained experiments. As a proof-of-concept, we recorded and wirelessly transmitted broadband neural data from 32 electrodes in premotor cortex while acquiring single-camera video of a rhesus macaque walking on a treadmill. We demonstrate the ability to extract behavioral kinematics using an automated computer vision algorithm without use of markers and to predict kinematics from the neural data. Together these advances suggest that a new class of "freely moving monkey" experiments should be possible and should help broaden our understanding of the neural control of movement.

Challenges and opportunities for next-generation intracortically based neural prostheses

Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.

A dynamical systems view of motor preparation: implications for neural prosthetic system design

Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached.