30,778 research outputs found
A Neural Circuit Model for Prospective Control of Interceptive Reaching
Two prospective controllers of hand movements in catching -- both based on required velocity control -- were simulated. Under certain conditions, this required velocity controlled to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint modeL Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.Vrije Universiteit (Gerrit-Jan van Jngen-Schenau stipend of the Faculty of Human Movement Sciences); Royal Netherlands Academy of Arts and Sciences; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409
A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection
A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and posterior parietal cortex can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
The very same thing: Extending the object token concept to incorporate causal constraints on individual identity
The contributions of feature recognition, object categorization, and recollection of episodic memories to the re-identification of a perceived object as the very same thing encountered in a previous perceptual episode are well understood in terms of both cognitive-behavioral phenomenology and neurofunctional implementation. Human beings do not, however, rely solely on features and context to re-identify individuals; in the presence of featural change and similarly-featured distractors, people routinely employ causal constraints to establish object identities. Based on available cognitive and neurofunctional data, the standard object-token based model of individual re-identification is extended to incorporate the construction of unobserved and hence fictive causal histories (FCHs) of observed objects by the pre-motor action planning system. Cognitive-behavioral and implementation-level predictions of this extended model and methods for testing them are outlined. It is suggested that functional deficits in the construction of FCHs are associated with clinical outcomes in both Autism Spectrum Disorders and later-stage stage Alzheimer's disease.\u
Neural Representations for Sensory-Motor Control, II: Learning a Head-Centered Visuomotor Representation of 3-D Target Position
A neural network model is described for how an invariant head-centered representation of 3-D target position can be autonomously learned by the brain in real time. Once learned, such a target representation may be used to control both eye and limb movements. The target representation is derived from the positions of both eyes in the head, and the locations which the target activates on the retinas of both eyes. A Vector Associative Map, or YAM, learns the many-to-one transformation from multiple combinations of eye-and-retinal position to invariant 3-D target position. Eye position is derived from outflow movement signals to the eye muscles. Two successive stages of opponent processing convert these corollary discharges into a. head-centered representation that closely approximates the azimuth, elevation, and vergence of the eyes' gaze position with respect to a cyclopean origin located between the eyes. YAM learning combines this cyclopean representation of present gaze position with binocular retinal information about target position into an invariant representation of 3-D target position with respect to the head. YAM learning can use a teaching vector that is externally derived from the positions of the eyes when they foveate the target. A YAM can also autonomously discover and learn the invariant representation, without an explicit teacher, by generating internal error signals from environmental fluctuations in which these invariant properties are implicit. YAM error signals are computed by Difference Vectors, or DVs, that are zeroed by the YAM learning process. YAMs may be organized into YAM Cascades for learning and performing both sensory-to-spatial maps and spatial-to-motor maps. These multiple uses clarify why DV-type properties are computed by cells in the parietal, frontal, and motor cortices of many mammals. YAMs are modulated by gating signals that express different aspects of the will-to-act. These signals transform a single invariant representation into movements of different speed (GO signal) and size (GRO signal), and thereby enable YAM controllers to match a planned action sequence to variable environmental conditions.National Science Foundation (IRI-87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-1309
Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways
Diverse classes of proteins function through large-scale conformational
changes; sophisticated enhanced sampling methods have been proposed to generate
these macromolecular transition paths. As such paths are curves in a
high-dimensional space, they have been difficult to compare quantitatively, a
prerequisite to, for instance, assess the quality of different sampling
algorithms. The Path Similarity Analysis (PSA) approach alleviates these
difficulties by utilizing the full information in 3N-dimensional trajectories
in configuration space. PSA employs the Hausdorff or Fr\'echet path
metrics---adopted from computational geometry---enabling us to quantify path
(dis)similarity, while the new concept of a Hausdorff-pair map permits the
extraction of atomic-scale determinants responsible for path differences.
Combined with clustering techniques, PSA facilitates the comparison of many
paths, including collections of transition ensembles. We use the closed-to-open
transition of the enzyme adenylate kinase (AdK)---a commonly used testbed for
the assessment enhanced sampling algorithms---to examine multiple microsecond
equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free
form alongside transition ensembles from the MD-based dynamic importance
sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting
algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for
instance, that differences in DIMS-MD and FRODA paths were mediated by a set of
conserved salt bridges whose charge-charge interactions are fully modeled in
DIMS-MD but not in FRODA. We also demonstrate how existing trajectory analysis
methods relying on pre-defined collective variables, such as native contacts or
geometric quantities, can be used synergistically with PSA, as well as the
application of PSA to more complex systems such as membrane transporter
proteins.Comment: 9 figures, 3 tables in the main manuscript; supplementary information
includes 7 texts (S1 Text - S7 Text) and 11 figures (S1 Fig - S11 Fig) (also
available from journal site
Evidence for a meteoritic origin of the September 15, 2007, Carancas crater
On September 15th, 2007, around 11:45 local time in Peru, near the Bolivian border, the
atmospheric entry of a meteoroid produced bright lights in the sky and intense detonations. Soon after,
a crater was discovered south of Lake Titicaca. These events have been detected by the Bolivian
seismic network and two infrasound arrays operating for the Comprehensive Nuclear-Test-Ban Treaty
Organization, situated at about 80 and 1620 km from the crater. The localization and origin time
computed with the seismic records are consistent with the reported impact. The entry elevation and
azimuthal angles of the trajectory are estimated from the observed signal time sequences and backazimuths.
From the crater diameter and the airwave amplitudes, the kinetic energy, mass and
explosive energy are calculated. Using the estimated velocity of the meteoroid and similarity criteria
between orbital elements, an association with possible parent asteroids is attempted. The favorable
setting of this event provides a unique opportunity to evaluate physical and kinematic parameters of
the object that generated the first actual terrestrial meteorite impact seismically recorded
Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities
Robotic assistance allows surgeons to perform dexterous and tremor-free
procedures, but robotic aid is still underrepresented in procedures with
constrained workspaces, such as deep brain neurosurgery and endonasal surgery.
In these procedures, surgeons have restricted vision to areas near the surgical
tooltips, which increases the risk of unexpected collisions between the shafts
of the instruments and their surroundings. In this work, our
vector-field-inequalities method is extended to provide dynamic
active-constraints to any number of robots and moving objects sharing the same
workspace. The method is evaluated with experiments and simulations in which
robot tools have to avoid collisions autonomously and in real-time, in a
constrained endonasal surgical environment. Simulations show that with our
method the combined trajectory error of two robotic systems is optimal.
Experiments using a real robotic system show that the method can autonomously
prevent collisions between the moving robots themselves and between the robots
and the environment. Moreover, the framework is also successfully verified
under teleoperation with tool-tissue interactions.Comment: Accepted on T-RO 2019, 19 Page
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
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