Electron-impact excitation of X 1Sigma_g⁺(v[double-prime]=0) to the a[double-prime] 1Sigma_g⁺, b 1Piu, c3 1Piu, o3 1Piu, b[prime] 1Sigma_u⁺, c₄^[prime] 1Sigma_u⁺, G 3Piu, and F 3Piu states of molecular nitrogen

Measurements of differential cross sections (DCSs) for electron-impact excitation of the a[double-prime] 1Sigmag+, b 1Piu, c3 1Piu, o3 1Piu, b[prime] 1Sigmau+, c4[prime] 1Sigmau+, G 3Piu, and F 3Piu states in N2 from the X 1Sigmag+(v[double-prime]=0) ground level are presented. The DCSs were obtained from energy-loss spectra in the region of 12 to 13.82 eV measured at incident energies of 17.5, 20, 30, 50, and 100 eV and for scattering angles ranging from 2° to 130°. The analysis of the spectra follows a different algorithm from that employed in a previous study of N2 for the valence states [Khakoo et al. Phys. Rev. A 71, 062703 (2005)], since the 1Piu and 1Sigmau+ states form strongly interacting Rydberg-valence series. The results are compared with existing data

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

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

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 excitability 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 ring 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

A brain machine interface control algorithm designed from a feedback control perspective

Abstract-We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alter the modeling assumptions made by these algorithms and the methods by which these algorithms are trained. In online neural control experiments recording from a 96-electrode array implanted in M1 of a macaque monkey, the ReFIT-KF control algorithm demonstrates large performance improvements over the current state of the art velocity Kalman filter, reducing target acquisition time by a factor of two, while maintaining a 500 ms hold period, thereby increasing the clinical viability of BMI systems

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

OBJECTIVE: Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. APPROACH: Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. MAIN RESULTS: LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. SIGNIFICANCE: These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs