Mixed Signals: On Separating Brain Signal from Noise

Separating signal from noise

Suppose that a sequence of numbers xn (a ‘signal’) is transmitted through a noisy channel. The receiver observes a noisy version of the signal with additive random fluctuations, xn + ξn, where ξn is a sequence of independent standard Gaussian random variables. Suppose further that the signal is known to come from some fixed space X of possible signals. Is it possible to fully recover the transm...

Separating Signal from Noise: Evaluating Emerging Technologies

Separating Sparse Signals from Correlated Noise in Binary Classification

Among the goals of statistical genetics is to find sparse associations of genetic data with binary phenotypes, such as heritable diseases. Often, the data are obfuscated by confounders such as age, ancestry, or population structure. A widely appreciated modeling paradigm which corrects for such confounding relies on linear mixed models. These are linear regression models with correlated noise, ...

IC Mixed-Signal BIST: Separating Facts from Fiction

Introduction The first papers that proposed on-chip mixed-signal built-in self-test (MS-BIST) were published about ten years ago [1]. The first commercial products were announced five years ago at ITC’97 – LogicVision’s adcBIST [2] and pllBIST [3]. The latter tests more PLL parameters than most companies do, and everything is done on-chip in 4K gates: stimulus, response analysis, and comparison...

Separating News from Noise

By treating mouse fibroblasts with varying concentrations of tumor necrosis factor (TNF) and measuring the resulting shifts in the production of transcription factors NFB and ATF-2, the researchers determined that individual cells are essentially capable of a one-bit response to this ligand, where signaling is simply on or ‘off.’ Many intermediate steps lie between TNF and NFB, and the authors ...