Topic
White noise
About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The expected firing probability of a stochastic neuron is approximated by a function of the expected subthreshold membrane potential, for the case of colored noise, in order to extend the recently proposed white noise model to conductance-based neurons.
Abstract: The expected firing probability of a stochastic neuron is approximated by a function of the expected subthreshold membrane potential, for the case of colored noise. We propose this approximation in order to extend the recently proposed white noise model [A. V. Chizhov and L. J. Graham, Phys. Rev. E 75, 011924 (2007)] to the case of colored noise, applying a refractory density approach to conductance-based neurons. The uncoupled neurons of a single population receive a common input and are dispersed by the noise. Within the framework of the model the effect of noise is expressed by the so-called hazard function, which is the probability density for a single neuron to fire given the average membrane potential in the presence of a noise term. To derive the hazard function we solve the Kolmogorov-Fokker-Planck equation for a mean voltage-driven neuron fluctuating due to colored noisy current. We show that a sum of both a self-similar solution for the case of slow changing mean voltage and a frozen stationary solution for fast changing mean voltage gives a satisfactory approximation for the hazard function in the arbitrary case. We demonstrate the quantitative effect of a temporal correlation of noisy input on the neuron dynamics in the case of leaky integrate-and-fire and detailed conductance-based neurons in response to an injected current step.
80 citations
••
21 Aug 2005TL;DR: This paper shows how to combine several simple techniques -- sketches (random projections), convolution, structured random vectors, grid structures, and combinatorial design -- to achieve high performance windowed Pearson correlation over a variety of data sets.
Abstract: Data arriving in time order (a data stream) arises in fields including physics, finance, medicine, and music, to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramatically as sensor technology improves. Further, the number of sensors is increasing, so correlating data between sensors becomes ever more critical in order to distill knowlege from the data. In many applications such as finance, recent correlations are of far more interest than long-term correlation, so correlation over sliding windows (windowed correlation) is the desired operation. Fast response is desirable in many applications (e.g., to aim a telescope at an activity of interest or to perform a stock trade). These three factors -- data size, windowed correlation, and fast response -- motivate this work.Previous work [10, 14] showed how to compute Pearson correlation using Fast Fourier Transforms and Wavelet transforms, but such techniques don't work for time series in which the energy is spread over many frequency components, thus resembling white noise. For such "uncooperative" time series, this paper shows how to combine several simple techniques -- sketches (random projections), convolution, structured random vectors, grid structures, and combinatorial design -- to achieve high performance windowed Pearson correlation over a variety of data sets.
80 citations
••
TL;DR: The ALE output is shown to be the sum of two uncorrelated components, one arising from optimum finite-lag Wiener filtering of the narrow-band input components, and the other arising from the misadjustment error associated with the adaptation process.
Abstract: The adaptive line enhancer (ALE) is an adaptive digital filter designed to suppress uncorrelated components of its input, while passing any narrow-band components with little attenuation. The purpose of this paper is to analyze the second-order output statistics of the ALE in steady-state operation, for input samples consisting of weak narrow-band signals in white Gaussian noise. The ALE output is shown to be the sum of two uncorrelated components, one arising from optimum finite-lag Wiener filtering of the narrow-band input components, and the other arising from the misadjustment error associated with the adaptation process. General expressions are given for the output auto-correlation function and power spectrum with arbitrary narrow-band input signals, and the case of a single sinusoid in white noise is worked out as an example. Finally, the significance of these results to practical applications of the ALE is mentioned.
80 citations
••
TL;DR: Strong consistency of the proposed estimator is proved under certain sufficient conditions and simulation results are presented in support of the theory.
80 citations
••
24 Mar 1996TL;DR: A dynamic bandwidth allocation strategy used to support VBR video traffic is proposed and predicts the bandwidth requirements for future frames using either adaptive or non-adaptive least mean square (LMS) error linear predictors.
Abstract: Variable bit rate (VBR) video traffic is expected to be one of the major applications that need to be supported by broadband packet-switched networks A dynamic bandwidth allocation strategy used to support VBR video traffic is proposed This strategy predicts the bandwidth requirements for future frames using either adaptive or non-adaptive least mean square (LMS) error linear predictors The adaptive technique does not require any prior knowledge of the statistics, nor assumes stationarity Several reservation schemes are also presented Analysis using six one-half hour video traces indicate that prediction errors for the bandwidth required for the next frame are almost white noise By reserving a bandwidth equal to the predicted value, only the prediction errors need to be buffered Because the errors are almost white noise, a small buffer size, high utilization, and a small delay are achieved Simulation results for 1-step linear predictor show that for the same expected cell loss, the buffer size is reduced by more than a factor of 100 and the network utilization is increased by more than 250% as compared to a fixed service rate
80 citations