Neuronal calcium sparks and intracellular calcium “noise���

Intracellular calcium ions are involved in many forms of cellular function. To accommodate so many control functions, a complex spatiotemporal organization of calcium signaling has developed. In both excitable and nonexcitable cells, calcium signaling was found to fluctuate. Sudden localized increases in the intracellular calcium concentration—or calcium sparks—were found in heart, striated and smooth muscle, Xenopus Laevis oocytes, and HeLa and P12 cells. In the nervous system, intracellular calcium ions were found important in key processes such as transmitter release, repetitive firing, and gene expression. Hence, we examined whether calcium sparks also exist in neurons. Using confocal laser-scanning microscopy and fluorescent probes, we found that calcium sparks exist in two types of neuronal preparations: the presynaptic boutons of the lizard neuromuscular junction and rat hippocampal neurons in cell culture. Control experiments exclude the possibility that these calcium sparks originate from instrumental or biological artifacts. Calcium sparks seem to be just the tip of the iceberg of a more general phenomenon of intracellular calcium “noise.” We speculate that calcium sparks and calcium noise may be of key importance in calcium signaling in the nervous system.

Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

To investigate the nature of plasticity in the adult visual system, perceptual learning was measured in a peripheral orientation discrimination task with systematically varying amounts of external (environmental) noise. The signal contrasts required to achieve threshold were reduced by a factor or two or more after training at all levels of external noise. The strong quantitative regularities revealed by this novel paradigm ruled out changes in multiplicative internal noise, changes in transducer nonlinearites, and simple attentional tradeoffs. Instead, the regularities specify the mechanisms of perceptual learning at the behavioral level as a combination of external noise exclusion and stimulus enhancement via additive internal noise reduction. The findings also constrain the neural architecture of perceptual learning. Plasticity in the weights between basic visual channels and decision is sufficient to account for perceptual learning without requiring the retuning of visual mechanisms.

Noise in neurons is message dependent

Neuronal responses are conspicuously variable. We focus on one particular aspect of that variability: the precision of action potential timing. We show that for common models of noisy spike generation, elementary considerations imply that such variability is a function of the input, and can be made arbitrarily large or small by a suitable choice of inputs. Our considerations are expected to extend to virtually any mechanism of spike generation, and we illustrate them with data from the visual pathway. Thus, a simplification usually made in the application of information theory to neural processing is violated: noise is not independent of the message. However, we also show the existence of error-correcting topologies, which can achieve better timing reliability than their components.

Titration of chaos with added noise

Deterministic chaos has been implicated in numerous natural and man-made complex phenomena ranging from quantum to astronomical scales and in disciplines as diverse as meteorology, physiology, ecology, and economics. However, the lack of a definitive test of chaos vs. random noise in experimental time series has led to considerable controversy in many fields. Here we propose a numerical titration procedure as a simple “litmus test” for highly sensitive, specific, and robust detection of chaos in short noisy data without the need for intensive surrogate data testing. We show that the controlled addition of white or colored noise to a signal with a preexisting noise floor results in a titration index that: (i) faithfully tracks the onset of deterministic chaos in all standard bifurcation routes to chaos; and (ii) gives a relative measure of chaos intensity. Such reliable detection and quantification of chaos under severe conditions of relatively low signal-to-noise ratio is of great interest, as it may open potential practical ways of identifying, forecasting, and controlling complex behaviors in a wide variety of physical, biomedical, and socioeconomic systems.

Intrinsic noise in gene regulatory networks

Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

Noise and PSRR improvement technique for TPC readout front-end in CMOS. technology.

ALICE is one of four major experiments of particle accelerator LHC installed in the European laboratory CERN. The management committee of the LHC accelerator has just approved a program update for this experiment. Among the upgrades planned for the coming years of the ALICE experiment is to improve the resolution and tracking efficiency maintaining the excellent particles identification ability, and to increase the read-out event rate to 100 KHz. In order to achieve this, it is necessary to update the Time Projection Chamber detector (TPC) and Muon tracking (MCH) detector modifying the read-out electronics, which is not suitable for this migration. To overcome this limitation the design, fabrication and experimental test of new ASIC named SAMPA has been proposed . This ASIC will support both positive and negative polarities, with 32 channels per chip and continuous data readout with smaller power consumption than the previous versions. This work aims to design, fabrication and experimental test of a readout front-end in 130nm CMOS technology with configurable polarity (positive/negative), peaking time and sensitivity. The new SAMPA ASIC can be used in both chambers (TPC and MCH). The proposed front-end is composed of a Charge Sensitive Amplifier (CSA) and a Semi-Gaussian shaper. In order to obtain an ASIC integrating 32 channels per chip, the design of the proposed front-end requires small area and low power consumption, but at the same time requires low noise. In this sense, a new Noise and PSRR (Power Supply Rejection Ratio) improvement technique for the CSA design without power and area impact is proposed in this work. The analysis and equations of the proposed circuit are presented which were verified by electrical simulations and experimental test of a produced chip with 5 channels of the designed front-end. The measured equivalent noise charge was <550e for 30mV/fC of sensitivity at a input capacitance of 18.5pF. The total core area of the front-end was 2300?m × 150?m, and the measured total power consumption was 9.1mW per channel.

Intrinsic spin noise in MgO magnetic tunnel junctions

We consider two intrinsic sources of noise in ultra-sensitive magnetic field sensors based on MgO magnetic tunnel junctions, coming both from 25 Mg nuclear spins (I = 5/2, 10% natural abundance) and S = 1 Mg-vacancies. While nuclear spins induce noise peaked in the MHz frequency range, the vacancies noise peaks in the GHz range. We find that the nuclear noise in submicron devices has a similar magnitude than the 1/f noise, while the vacancy-induced noise dominates in the GHz range. Interestingly, the noise spectrum under a finite magnetic field gradient may provide spatial information about the spins in the MgO layer.

A parallel method for impulsive image noise removal on hybrid CPU/GPU systems

A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance is evaluated in terms of execution time and a comparison of the implementation parallelised in multi-core, GPUs and the combination of both is conducted. A performance analysis with large images is conducted in order to identify the amount of pixels to allocate in the CPU and GPU. The observed time shows that both devices must have work to do, leaving the most to the GPU. Results show that parallel implementations of denoising filters on GPUs and multi-cores are very advisable, and they open the door to use such algorithms for real-time processing.

Image Noise Removal on Heterogeneous CPU-GPU Configurations

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.

Noise-tolerance feasibility for restricted-domain Information Retrieval systems

Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented.