7,425 research outputs found
The Perception of Globally Coherent Motion
How do human observers perceive a coherent pattern of motion from a disparate set of local motion measures? Our research has examined how ambiguous motion signals along straight contours are spatially integrated to obtain a globally coherent perception of motion. Observers viewed displays containing a large number of apertures, with each aperture containing one or more contours whose orientations and velocities could be independently specified. The total pattern of the contour trajectories across the individual apertures was manipulated to produce globally coherent motions, such as rotations, expansions, or translations. For displays containing only straight contours extending to the circumferences of the apertures, observers' reports of global motion direction were biased whenever the sampling of contour orientations was asymmetric relative to the direction of motion. Performance was improved by the presence of identifiable features, such as line ends or crossings, whose trajectories could be tracked over time. The reports of our observers were consistent with a pooling process involving a vector average of measures of the component of velocity normal to contour orientation, rather than with the predictions of the intersection-of-constraints analysis in velocity space.Air Force Office of Scientific Research (90-0175, 89-0016); National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research (BNS-8908426
Temporal structure in spiking patterns of ganglion cells defines perceptual thresholds in rodents with subretinal prosthesis.
Subretinal prostheses are designed to restore sight in patients blinded by retinal degeneration using electrical stimulation of the inner retinal neurons. To relate retinal output to perception, we studied behavioral thresholds in blind rats with photovoltaic subretinal prostheses stimulated by full-field pulsed illumination at 20 Hz, and measured retinal ganglion cell (RGC) responses to similar stimuli ex-vivo. Behaviorally, rats exhibited startling response to changes in brightness, with an average contrast threshold of 12%, which could not be explained by changes in the average RGC spiking rate. However, RGCs exhibited millisecond-scale variations in spike timing, even when the average rate did not change significantly. At 12% temporal contrast, changes in firing patterns of prosthetic response were as significant as with 2.3% contrast steps in visible light stimulation of healthy retinas. This suggests that millisecond-scale changes in spiking patterns define perceptual thresholds of prosthetic vision. Response to the last pulse in the stimulation burst lasted longer than the steady-state response during the burst. This may be interpreted as an excitatory OFF response to prosthetic stimulation, and can explain behavioral response to decrease in illumination. Contrast enhancement of images prior to delivery to subretinal prosthesis can partially compensate for reduced contrast sensitivity of prosthetic vision
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Temporal Evolution of Both Premotor and Motor Cortical Tuning Properties Reflect Changes in Limb Biomechanics
A prevailing theory in the cortical control of limb movement posits that premotor cortex initiates a high-level motor plan that is transformed by the primary motor cortex (MI) into a low-level motor command to be executed. This theory implies that the premotor cortex is shielded from the motor periphery and therefore its activity should not represent the low-level features of movement. Contrary to this theory, we show that both dorsal (PMd) and ventral premotor (PMv) cortices exhibit population-level tuning properties that reflect the biomechanical properties of the periphery similar to those observed in M1. We recorded single-unit activity from M1, PMd, and PMv and characterized their tuning properties while six rhesus macaques performed a reaching task in the horizontal plane. Each area exhibited a bimodal distribution of preferred directions during execution consistent with the known biomechanical anisotropies of the muscles and limb segments. Moreover, these distributions varied in orientation or shape from planning to execution. A network model shows that such population dynamics are linked to a change in biomechanics of the limb as the monkey begins to move, specifically to the state-dependent properties of muscles. We suggest that, like M1, neural populations in PMd and PMv are more directly linked with the motor periphery than previously thought
Deep neural networks for direct, featureless learning through observation: the case of 2d spin models
We demonstrate the capability of a convolutional deep neural network in
predicting the nearest-neighbor energy of the 4x4 Ising model. Using its
success at this task, we motivate the study of the larger 8x8 Ising model,
showing that the deep neural network can learn the nearest-neighbor Ising
Hamiltonian after only seeing a vanishingly small fraction of configuration
space. Additionally, we show that the neural network has learned both the
energy and magnetization operators with sufficient accuracy to replicate the
low-temperature Ising phase transition. We then demonstrate the ability of the
neural network to learn other spin models, teaching the convolutional deep
neural network to accurately predict the long-range interaction of a screened
Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian,
and a modified Potts model Hamiltonian. In the case of the long-range
interaction, we demonstrate the ability of the neural network to recover the
phase transition with equivalent accuracy to the numerically exact method.
Furthermore, in the case of the long-range interaction, the benefits of the
neural network become apparent; it is able to make predictions with a high
degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact
calculation. Additionally, we demonstrate how the neural network succeeds at
these tasks by looking at the weights learned in a simplified demonstration
Applying Deep Learning to Fast Radio Burst Classification
Upcoming Fast Radio Burst (FRB) surveys will search 10\, beams on
sky with very high duty cycle, generating large numbers of single-pulse
candidates. The abundance of false positives presents an intractable problem if
candidates are to be inspected by eye, making it a good application for
artificial intelligence (AI). We apply deep learning to single pulse
classification and develop a hierarchical framework for ranking events by their
probability of being true astrophysical transients. We construct a tree-like
deep neural network (DNN) that takes multiple or individual data products as
input (e.g. dynamic spectra and multi-beam detection information) and trains on
them simultaneously. We have built training and test sets using false-positive
triggers from real telescopes, along with simulated FRBs, and single pulses
from pulsars. Training of the DNN was independently done for two radio
telescopes: the CHIME Pathfinder, and Apertif on Westerbork. High accuracy and
recall can be achieved with a labelled training set of a few thousand events.
Even with high triggering rates, classification can be done very quickly on
Graphical Processing Units (GPUs). That speed is essential for selective
voltage dumps or issuing real-time VOEvents. Next, we investigate whether
dedispersion back-ends could be completely replaced by a real-time DNN
classifier. It is shown that a single forward propagation through a moderate
convolutional network could be faster than brute-force dedispersion; but the
low signal-to-noise per pixel makes such a classifier sub-optimal for this
problem. Real-time automated classification may prove useful for bright,
unexpected signals, both now and in the era of radio astronomy when data
volumes and the searchable parameter spaces further outgrow our ability to
manually inspect the data, such as for SKA and ngVLA
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