Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey\u27s arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback

Improving Brain–Machine Interface Performance by Decoding Intended Future Movements

Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance

Virus isolation studies suggest short-term variations in abundance in natural cyanophage populations of the Indian Ocean

Cyanophage abundance has been shown to fluctuate over long timescales and with depth, but little is known about how it varies over short timescales. Previous short-term studies have relied on counting total virus numbers and therefore the phages which infect cyanobacteria cannot be distinguished from the total count. In this study, an isolation-based approach was used to determine cyanophage abundance from water samples collected over a depth profile for a 24 h period from the Indian Ocean. Samples were used to infect Synechococcus sp. WH7803 and the number of plaque forming units (pfu) at each time point and depth were counted. At 10 m phage numbers were similar for most time-points, but there was a distinct peak in abundance at 0100 hours. Phage numbers were lower at 25 m and 50 m and did not show such strong temporal variation. No phages were found below this depth. Therefore, we conclude that only the abundance of phages in surface waters showed a clear temporal pattern over a short timescale. Fifty phages from a range of depths and time points were isolated and purified. The molecular diversity of these phages was estimated using a section of the phage-encoded psbD gene and the results from a phylogenetic analysis do not suggest that phages from the deeper waters form a distinct subgroup

The Random Feature Model for Input-Output Maps between Banach Spaces

Well known to the machine learning community, the random feature model, originally introduced by Rahimi and Recht in 2008, is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to the real line. In this paper, we instead propose a methodology for use of the random feature model as a data-driven surrogate for operators that map an input Banach space to an output Banach space. Although the methodology is quite general, we consider operators defined by partial differential equations (PDEs); here, the inputs and outputs are themselves functions, with the input parameters being functions required to specify the problem, such as initial data or coefficients, and the outputs being solutions of the problem. Upon discretization, the model inherits several desirable attributes from this infinite-dimensional, function space viewpoint, including mesh-invariant approximation error with respect to the true PDE solution map and the capability to be trained at one mesh resolution and then deployed at different mesh resolutions. We view the random feature model as a non-intrusive data-driven emulator, provide a mathematical framework for its interpretation, and demonstrate its ability to efficiently and accurately approximate the nonlinear parameter-to-solution maps of two prototypical PDEs arising in physical science and engineering applications: viscous Burgers' equation and a variable coefficient elliptic equation

The Random Feature Model for Input-Output Maps between Banach Spaces

Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to the real line. In this paper, we instead propose a methodology for use of the random feature model as a data-driven surrogate for operators that map an input Banach space to an output Banach space. Although the methodology is quite general, we consider operators defined by partial differential equations (PDEs); here, the inputs and outputs are themselves functions, with the input parameters being functions required to specify the problem, such as initial data or coefficients, and the outputs being solutions of the problem. Upon discretization, the model inherits several desirable attributes from this infinite-dimensional viewpoint, including mesh-invariant approximation error with respect to the true PDE solution map and the capability to be trained at one mesh resolution and then deployed at different mesh resolutions. We view the random feature model as a non-intrusive data-driven emulator, provide a mathematical framework for its interpretation, and demonstrate its ability to efficiently and accurately approximate the nonlinear parameter-to-solution maps of two prototypical PDEs arising in physical science and engineering applications: viscous Burgers' equation and a variable coefficient elliptic equation.Comment: To appear in SIAM Journal on Scientific Computing; 32 pages, 9 figure

Probing the Masses of the PSR J0621+1002 Binary System Through Relativistic Apsidal Motion

Orbital, spin and astrometric parameters of the millisecond pulsar PSR J0621+1002 have been determined through six years of timing observations at three radio telescopes. The chief result is a measurement of the rate of periastron advance, omega_dot = 0.0116 +/- 0.0008 deg/yr. Interpreted as a general relativistic effect, this implies the sum of the pulsar mass, m_1, and the companion mass, m_2, to be M = m_1 + m_2 = 2.81 +/- 0.30 msun. The Keplerian parameters rule out certain combinations of m_1 and m_2, as does the non-detection of Shapiro delay in the pulse arrival times. These constraints, together with the assumption that the companion is a white dwarf, lead to the 68% confidence maximum likelihood values of m_1 = 1.70(+0.32 -0.29) msun and m_2 =0.97(+0.27 - 0.15) msun and to the 95% confidence maximum likelihood values of m_1 = 1.70(+0.59 -0.63) msun and m_2 = 0.97(+0.43 -0.24) msun. The other major finding is that the pulsar experiences dramatic variability in its dispersion measure (DM), with gradients as steep as 0.013 pc cm^{-3} / yr. A structure function analysis of the DM variations uncovers spatial fluctuations in the interstellar electron density that cannot be fit to a single power law, unlike the Kolmogorov turbulent spectrum that has been seen in the direction of other pulsars. Other results from the timing analysis include the first measurements of the pulsar's proper motion, mu = 3.5 +/- 0.3 mas / yr, and of its spin-down rate, dP/dt = 4.7 x 10^{-20}, which, when corrected for kinematic biases and combined with the pulse period, P = 28.8 ms, gives a characteristic age of 1.1 x 10^{10} yr and a surface magnetic field strength of 1.2 x 10^{9} G.Comment: Accepted by ApJ, 10 pages, 5 figure

Stratigraphic and Ecologic Implications of Late Precambrian Microfossils from Utah

Abundant and well preserved microfossils have been discovered in black shales and siltstones of the glaciogenic upper Precambrian Mineral Fork Formation near Salt Lake City, Utah. The rocks are interpreted to have been deposited close to an ice margin in a shallow, restricted marine embayment during glacial retreat. Several distinct morphotypes are discernible, but following Moorman (1974), we interpret most of these as stages in the life cycle of a single planktonic, endosporulating alga, Bavlinella faveolata (Shepeleva) Vidal. The low taxonomic diversity of this assemblage, coupled with the large population size and almost complete dominance by Bavlinella, suggests an ecologically stressed environment. The source of this stress was probably the melting glacier. On the basis of stratigraphic position and lithological correlation with a radiometrically dated sequence in Washington, the Mineral Fork Formation has been considered to be about 800 m.y. old; however, most well dated occurrences of Bavlinella are in rocks of Vendian (650-570 m.y.) age. The presence of Bavlinella faveolata in Mineral Fork strata raises significant questions concerning both the stratigraphic range of this presumed Vendian index fossil and the timing of Late Precambrian glaciation in the North American Cordillera

Stratigraphic Controls on a Salt-Withdrawal Intraslope Minibasin, North-Central Green Canyon, Gulf of Mexico: Implications for Misinterpreting Sea Level Change

Three-dimensional seismic data from the Fuji basin, a salt-controlled intraslope minibasin in north-central Green Canyon, Gulf of Mexico, reveal complex interactions between gravity- and suspension-driven sedimentation. Seismic volumes for late Pleistocene (sim470 ka) to Holocene fill within the Fuji basin consist of approximately 45% mass transport complexes (MTCs), 5% channelized sandy turbidites, and 50% hemipelagites and muddy turbidites. At least ten MTCs within the Fuji basin flowed radially toward its depocenter, either from basin flanks (i.e., intrabasinal) or as a result of larger-scale salt motion (i.e., extrabasinal). Sediment transport directions are inferred on the basis of elongate basal incisions and smaller-scale scours, head scarps, fold orientation within the complexes, and stratigraphic thinning trends at downdip margins. An amalgamated set of three channelized sandy turbidite complexes less than 350 m (1148 ft) thick and 3 km (1.8 mi) across represents the main sand delivery pathway into the Fuji basin. These deposits are thought to be due to shelf bypass, and possibly, to proximity to the Pleistocene shoreline. Hemipelagites and muddy turbidites are homogeneous, and their thickness is relatively consistent at basin scale. This facies represents background sedimentation. A process-driven model has been developed involving halokinetic autocyclicity as the primary control on sedimentation in the Fuji basin. Passive salt motion accounts better for both the directions of sediment transport and the frequency of late Pleistocene–Holocene MTCs than currently popular eustatic and steady-state bathymetric models. The conclusion is significant in casting doubt on the generally assumed importance of eustasy in controlling off-shelf lowstand sedimentation and in implying marked variations in stratigraphic details at length scales of less than 10 km (6.2 mi)