François Chapeau-Blondeau

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We address the problem of synthesizing a generalized Gaussian noise with exponent 1/2 by means of a nonlinear memoryless transformation applied to a uniform noise. We show that this transformation is expressable in terms of a special function known under the name of the Lambert W function. We review the main methods for numerical evaluation of the relevant(More)
We compare two simple test statistics that a detector can compute from multiple noisy data in a binary decision problem based on a maximum a posteriori probability (MAP) criterion. One of these statistics is the standard sample mean of the data (linear detector), which allows one to minimize the probability of detection error when the noise is Gaussian. The(More)
Stochastic resonance is a phenomenon whereby the transmission of a signal by certain nonlinear systems can be improved by addition of noise. We propose a brief overview of this effect, together with an extension based on informationtheoretic concepts. We analyze various conditions of nonlinear transmission where the input–output Shannon mutual information,(More)
The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the(More)
In this paper we consider simple neural network models consisting oftwo to three continuons nonlinear neurons, with no intrinsic synaptic plasticity, and with delay in neural signal transmission. We investigate thé différent dynamic régimes which may exist for thèse "minimal" neural network structures. Examples of stable, oscillatory (periodic or(More)
We compare the performance of two detection schemes in charge of detecting the presence of a signal buried in an additive noise. One of these is the correlation receiver (linear detector), which is optimal when the noise is Gaussian. The other detector is obtained by applying the same correlation receiver to the output of a nonlinear preprocessor formed by(More)
A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers, in which all the quantizer outputs are added to produce the array output. This parsimonious signal representation is used to implement an optimal detection from the output of the array. Such conditions can be relevant for fast real-time processing in largescale(More)
Using signal processing measures we evaluate the effect of aging on the peripheral cardiovascular system. Laser Doppler flowmetry (LDF) signals, reflecting the microvascular perfusion, are recorded on the forearm of 27 healthy subjects between 20-30, 40-50, or 60-70 years old. Wavelet-based representations, Hölder exponents, and sample entropy values are(More)
We analyze the parametric estimation that can be performed on a signal buried in noise based on the parsimonious representation provided by a parallel array of threshold devices. The Fisher information contained in the array output about the input parameter is used as the measure of performance in the estimation task. For estimation on a suprathreshold(More)
A stochastic resonance effect, under the form of a noise-improved performance, is shown feasible for a whole range of optimal detection strategies, including Bayesian, minimum error-probability, Neyman–Pearson, and minimax detectors. In each case, situations are demonstrated where the performance of the optimal detector can be improved (locally) by raising(More)