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.

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data.

MOTIVATION: Recent advances in flow cytometry enable simultaneous single-cell measurement of 30+ surface and intracellular proteins. CytoSPADE is a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events algorithm for tree-based analysis and visualization of this high-dimensional cytometry data. AVAILABILITY: Source code and binaries are freely available at http://cytospade.org and via Bioconductor version 2.10 onwards for Linux, OSX and Windows. CytoSPADE is implemented in R, C++ and Java. CONTACT: michael.linderman@mssm.edu SUPPLEMENTARY INFORMATION: Additional documentation available at http://cytospade.org.

A mm-sized wirelessly powered and remotely controlled locomotive implant.

A wirelessly powered and controlled implantable device capable of locomotion in a fluid medium is presented. Two scalable low-power propulsion methods are described that achieve roughly an order of magnitude better performance than existing methods in terms of thrust conversion efficiency. The wireless prototype occupies 0.6 mm × 1 mm in 65 nm CMOS with an external 2 mm × 2 mm receive antenna. The IC consists of a matching network, a rectifier, a bandgap reference, a regulator, a demodulator, a digital controller, and high-current drivers that interface directly with the propulsion system. It receives 500 µW from a 2 W 1.86 GHz power signal at a distance of 5 cm. Asynchronous pulse-width modulation on the carrier allows for data rates from 2.5-25 Mbps with energy efficiency of 0.5 pJ/b at 10 Mbps. The received data configures the propulsion system drivers, which are capable of driving up to 2 mA at 0.2 V and can achieve speed of 0.53 cm/sec in a 0.06 T magnetic field.

Translating electromagnetic torque into controlled motion for use in medical implants.

A new propulsion method for sub-millimeter implants is presented that achieves high power to thrust conversion efficiency with a simple implementation. Previous research has shown that electromagnetic forces are a promising micro-scale propulsion mechanism; however the actual implementation is challenging due to the inherent symmetry of these forces. The presented technique translates torque into controlled motion via asymmetries in resistance forces, such as fluid drag. For a 1-mm sized object using this technique, the initial analysis predicts that speeds of 1 cm/sec can be achieved with approximately 100 µW of power, which is about 10 times more efficient than existing methods. In addition to better performance, this method is easily controllable and has favorable scalability.

HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications.

HermesD is a high-rate, low-power wireless transmission system to aid research in neural prosthetic systems for motor disabilities and basic motor neuroscience. It is the third generation of our "Hermes systems" aimed at recording and transmitting neural activity from brain-implanted electrode arrays. This system supports the simultaneous transmission of 32 channels of broadband data sampled at 30 ks/s, 12 b/sample, using frequency-shift keying modulation on a carrier frequency adjustable from 3.7 to 4.1 GHz, with a link range extending over 20 m. The channel rate is 24 Mb/s and the bit stream includes synchronization and error detection mechanisms. The power consumption, approximately 142 mW, is low enough to allow the system to operate continuously for 33 h, using two 3.6-V/1200-mAh Li-SOCl2 batteries. The transmitter was designed using off-the-shelf components and is assembled in a stack of three 28 mm ? 28-mm boards that fit in a 38 mm ? 38 mm ? 51-mm aluminum enclosure, a significant size reduction over the initial version of HermesD. A 7-dBi circularly polarized patch antenna is used as the transmitter antenna, while on the receiver side, a 13-dBi circular horn antenna is employed. The advantages of using circularly polarized waves are analyzed and confirmed by indoor measurements. The receiver is a stand-alone device composed of several submodules and is interfaced to a computer for data acquisition and processing. It is based on the superheterodyne architecture and includes automatic frequency control that keeps it optimally tuned to the transmitter frequency. The HermesD communications performance is shown through bit-error rate measurements and eye-diagram plots. The sensitivity of the receiver is -83 dBm for a bit-error probability of 10(-9). Experimental recordings from a rhesus monkey conducting multiple tasks show a signal quality comparable to commercial acquisition systems, both in the low-frequency (local field potentials) and upper-frequency bands (action potentials) of the neural signals. This system can be easily scaled up in terms of the number of channels and data rate to accommodate future generations of Hermes systems.

Towards Program Optimization through Automated Analysis of Numerical Precision.

Reducing the arithmetic precision of a computation has real performance implications, including increased speed, decreased power consumption, and a smaller memory footprint. For some architectures, e.g., GPUs, there can be such a large performance difference that using reduced precision is effectively a requirement. The tradeoff is that the accuracy of the computation will be compromised. In this paper we describe a proof assistant and associated static analysis techniques for efficiently bounding numerical and precision-related errors. The programmer/compiler can use these bounds to numerically verify and optimize an application for different input and machine configurations. We present several case study applications that demonstrate the effectiveness of these techniques and the performance benefits that can be achieved with rigorous precision analysis.

High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers.

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20-50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.

Locomotive micro-implant with active electromagnetic propulsion.

An active locomotive technique requiring only an external power source and a static magnetic field is presented, and its operation is analyzed and simulated. For a modest static MRI magnetic field of 1 T, the results show that a 1-mm cube achieves roughly 3 cm/sec of lateral motion using less than 20.4 microW of power. Current-carrying wires generate the forces, resulting in highly controllable motion. Existing solutions trade off size and power: passive solutions are small but impractical, and mechanical solutions are inefficient and large. The presented solution captures the advantages of both systems, and has much better scalability.

Adaptive resolution ADC array for neural implant.

This paper describes an ADC array for an implantable prosthetic processor which digitizes neural signals sensed by a microelectrode array. The ADC array consists of 96 variable resolution ADC base cells. The base ADC has been implemented in 0.13 microm CMOS as a 100kS/s SAR ADC whose resolution can be varied from 3 to 8-bits with corresponding power consumption of 0.23 microW to 0.90 microW achieving an ENOB of 7.8 at the 8-bit setting. The resolution of each ADC cell in the array is varied according to neural data content of the signal from the corresponding electrode. Resolution adaptation reduces power consumption by a factor of 2.3 whilst maintaining an effective 7.8-bit resolution across all channels.

Detecting neural-state transitions using hidden Markov models for motor cortical prostheses.

Neural prosthetic interfaces use neural activity related to the planning and perimovement epochs of arm reaching to afford brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or perimovement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and perimovement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model (HMM), in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an HMM, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity that is not accompanied by any external behavior changes, can be detected using a threshold on the a posteriori HMM state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as that by a maximum-likelihood estimator using a window of known plan activity. In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data. Thus the HMM technique for automatically detecting transitions between epochs of neural activity enables prosthetic interfaces that can operate autonomously.

HermesB: a continuous neural recording system for freely behaving primates.

Chronically implanted electrode arrays have enabled a broad range of advances in basic electrophysiology and neural prosthetics. Those successes motivate new experiments, particularly, the development of prototype implantable prosthetic processors for continuous use in freely behaving subjects, both monkeys and humans. However, traditional experimental techniques require the subject to be restrained, limiting both the types and duration of experiments. In this paper, we present a dual-channel, battery-powered neural recording system with an integrated three-axis accelerometer for use with chronically implanted electrode arrays in freely behaving primates. The recording system called HermesB, is self-contained, autonomous, programmable, and capable of recording broadband neural (sampled at 30 kS/s) and acceleration data to a removable compact flash card for up to 48 h. We have collected long-duration data sets with HermesB from an adult macaque monkey which provide insight into time scales and free behaviors inaccessible under traditional experiments. Variations in action potential shape and root-mean square (RMS) noise are observed across a range of time scales. The peak-to-peak voltage of action potentials varied by up to 30% over a 24-h period including step changes in waveform amplitude (up to 25%) coincident with high acceleration movements of the head. These initial results suggest that spike-sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance. During physically active periods (defined by head-mounted accelerometer), significantly reduced 5-25-Hz local field potential (LFP) power and increased firing rate variability were observed. Using a threshold fit to LFP power, 93% of 403 5-min recording blocks were correctly classified as active or inactive, potentially providing an efficient tool for identifying different behavioral contexts in prosthetic applications. These results demonstrate the utility of the HermesB system and motivate using this type of system to advance neural prosthetics and electrophysiological experiments.

Optimal operating frequency in wireless power transmission for implantable devices.

This paper examines short-range wireless powering for implantable devices and shows that existing analysis techniques are not adequate to conclude the characteristics of power transfer efficiency over a wide frequency range. It shows, theoretically and experimentally, that the optimal frequency for power transmission in biological media can be in the GHz-range while existing solutions exclusively focus on the MHz-range. This implies that the size of the receive coil can be reduced by 10(4) times which enables the realization of fully integrated implantable devices.

Mixture of trajectory models for neural decoding of goal-directed movements.

Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.

Neural recording stability of chronic electrode arrays in freely behaving primates.

Chronically implanted electrode arrays have enabled a broad range of advances, particularly in the field of neural prosthetics. Those successes motivate development of prototype implantable prosthetic processors for long duration, continuous use in freely behaving subjects. However, traditional experimental protocols have provided limited information regarding the stability of the electrode arrays and their neural recordings. In this paper we present preliminary results derived from long duration neural recordings in a freely behaving primate which show variations in action potential shape and RMS noise across a range of time scales. These preliminary results suggest that spike sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance.

Multiday electrophysiological recordings from freely behaving primates.

Continuous multiday broadband neural data provide a means for observing effects at fine timescales over long periods. In this paper we present analyses on such data sets to demonstrate neural correlates for physically active and inactive time periods, as defined by the response of a head-mounted accelerometer. During active periods, we found that 5-25 Hz local field potential (LFP) power was significantly reduced, firing rate variability increased, and firing rates have greater temporal correlation. Using a single threshold fit to LFP power, 93% of the 403 5 minute blocks tested were correctly classified as active or inactive (as labeled by thresholding each block's maximal accelerometer magnitude). These initial results motivate the use of such data sets for testing neural prosthetics systems and for finding the neural correlates of natural behaviors.

Increasing the performance of cortically-controlled prostheses.

Neural prostheses have received considerable attention due to their potential to dramatically improve the quality of life of severely disabled patients. Cortically-controlled prostheses are able to translate neural activity from cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. Non-invasive and invasive electrode techniques can be used to measure neural activity, with the latter promising considerably higher levels of performance and therefore functionality to patients. We review here some of our recent experimental and computational work aimed at establishing a principled design methodology to increase electrode-based cortical prosthesis performance to near theoretical limits. Studies discussed include translating unprecedentedly brief periods of "plan" activity into high information rate (6.5 bits/s)control signals, improving decode algorithms and optimizing visual target locations for further performance increases, and recording from chronically implanted arrays in freely behaving monkeys to characterize neuron stability. Taken together, these results should substantially increase the clinical viability of cortical prostheses.

An autonomous, broadband, multi-channel neural recording system for freely behaving primates.

Successful laboratory proof-of-concept experiments with neural prosthetic systems motivate continued algorithm and hardware development. For these efforts to move beyond traditional fixed laboratory setups, new tools are needed to enable broadband, multi-channel, long duration neural recording from freely behaving primates. In this paper we present a dual-channel, battery powered, neural recording system with integrated 3-axis accelerometer for use with chronically implanted electrode arrays. The recording system, called HermesB, is self-contained, autonomous, programmable and capable of recording broadband neural and head acceleration data to a removable compact flash card for up to 48 hours.

Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems.

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

Model-based neural decoding of reaching movements: a maximum likelihood approach.

A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.

Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems.

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that several state-of-the-art spike sorting algorithms implemented in modern CMOS VLSI processes are expected to be power realistic.