California Institute of Technology

About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.

Neuronal responses to static texture patterns in area V1 of the alert macaque monkey

Abstract: 1. We recorded responses from neurons in area V1 of the alert macaque monkey to textured patterns modeled after stimuli used in psychophysical experiments of pop-out. Neuronal responses to a single...

The Phase of Ongoing EEG Oscillations Predicts Visual Perception

Abstract: Oscillations are ubiquitous in electrical recordings of brain activity. While the amplitude of ongoing oscillatory activity is known to correlate with various aspects of perception, the influence of oscillatory phase on perception remains unknown. In particular, since phase varies on a much faster timescale than the more sluggish amplitude fluctuations, phase effects could reveal the fine-grained neural mechanisms underlying perception. We presented brief flashes of light at the individual luminance threshold while EEG was recorded. Although the stimulus on each trial was identical, subjects detected approximately half of the flashes (hits) and entirely missed the other half (misses). Phase distributions across trials were compared between hits and misses. We found that shortly before stimulus onset, each of the two distributions exhibited significant phase concentration, but at different phase angles. This effect was strongest in the theta and alpha frequency bands. In this time-frequency range, oscillatory phase accounted for at least 16% of variability in detection performance and allowed the prediction of performance on the single-trial level. This finding indicates that the visual detection threshold fluctuates over time along with the phase of ongoing EEG activity. The results support the notion that ongoing oscillations shape our perception, possibly by providing a temporal reference frame for neural codes that rely on precise spike timing.

Ray tracing volume densities

Abstract: This paper presents new algorithms to trace objects represented by densities within a volume grid, e.g. clouds, fog, flames, dust, particle systems. We develop the light scattering equations, discuss previous methods of solution, and present a new approximate solution to the full three-dimensional radiative scattering problem suitable for use in computer graphics. Additionally we review dynamical models for clouds used to make an animated movie.

NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

Abstract: Accurate signal recovery or image reconstruction from indirect and possibly undersampled data is a topic of considerable interest; for example, the literature in the recent field of compressed sensing is already quite immense. This paper applies a smoothing technique and an accelerated first-order algorithm, both from Nesterov [Math. Program. Ser. A, 103 (2005), pp. 127-152], and demonstrates that this approach is ideally suited for solving large-scale compressed sensing reconstruction problems as (1) it is computationally efficient, (2) it is accurate and returns solutions with several correct digits, (3) it is flexible and amenable to many kinds of reconstruction problems, and (4) it is robust in the sense that its excellent performance across a wide range of problems does not depend on the fine tuning of several parameters. Comprehensive numerical experiments on realistic signals exhibiting a large dynamic range show that this algorithm compares favorably with recently proposed state-of-the-art methods. We also apply the algorithm to solve other problems for which there are fewer alternatives, such as total-variation minimization and convex programs seeking to minimize the $\ell_1$ norm of $Wx$ under constraints, in which $W$ is not diagonal. The code is available online as a free package in the MATLAB language.