Results 1  10
of
10
Stochastic resonance in continuous and spiking neuron models with levy noise. Neural Networks
 IEEE Transactions on
, 2008
"... Abstract—Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends standard Brownian noise to many types of impulsive jumpnoise processes found in real and model neurons as well as in models of finance and other random phenomena. Two new theorems and the Itô calculus show ..."
Abstract

Cited by 6 (6 self)
 Add to MetaCart
Abstract—Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends standard Brownian noise to many types of impulsive jumpnoise processes found in real and model neurons as well as in models of finance and other random phenomena. Two new theorems and the Itô calculus show that white Levy noise will benefit subthreshold neuronal signal detection if the noise process’s scaled drift velocity falls inside an interval that depends on the threshold values. These results generalize earlier “forbidden interval ” theorems of neuronal “stochastic resonance ” (SR) or noiseinjection benefits. Global and local Lipschitz conditions imply that additive white Levy noise can increase the mutual information or bit count of several feedback neuron models that obey a general stochastic differential equation (SDE). Simulation results show that the same noise benefits still occur for some infinitevariance stable Levy noise processes even though the theorems themselves apply only to finitevariance Levy noise. The Appendix proves the two Itôtheoretic lemmas that underlie the new Levy noisebenefit theorems. Index Terms—Levy noise, jump diffusion, mutual information, neuron models, signal detection, stochastic resonance (SR). I. STOCHASTIC RESONANCE IN NEURAL SIGNAL DETECTION STOCHASTIC RESONANCE (SR) occurs when noise benefits a system rather than harms it. Small amounts of noise can often enhance some forms of nonlinear signal processing
Adaptive stochastic resonance in noisy neurons based on mutual information
 IEEE Trans. Neural Netw
, 2004
"... Abstract—Noise can improve how memoryless neurons process signals and maximize their throughput information. Such favorable use of noise is the socalled “stochastic resonance ” or SR effect at the level of threshold neurons and continuous neurons. This paper presents theoretical and simulation evid ..."
Abstract

Cited by 6 (5 self)
 Add to MetaCart
Abstract—Noise can improve how memoryless neurons process signals and maximize their throughput information. Such favorable use of noise is the socalled “stochastic resonance ” or SR effect at the level of threshold neurons and continuous neurons. This paper presents theoretical and simulation evidence that 1) lone noisy threshold and continuous neurons exhibit the SR effect in terms of the mutual information between random input and output sequences, 2) a new statistically robust learning law can find this entropyoptimal noise level, and 3) the adaptive SR effect is robust against highly impulsive noise with infinite variance. Histograms estimate the relevant probability density functions at each learning iteration. A theorem shows that almost all noise probability density functions produce some SR effect in threshold neurons even if the noise is impulsive and has infinite variance. The optimal noise level in threshold neurons also behaves nonlinearly as the input
Optimal Noise Benefits in Neyman–Pearson and InequalityConstrained Statistical Signal Detection
"... Abstract—We present theorems and an algorithm to find optimal or nearoptimal “stochastic resonance ” (SR) noise benefits for Neyman–Pearson hypothesis testing and for more general inequalityconstrained signal detection problems. The optimal SR noise distribution is just the randomization of two no ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Abstract—We present theorems and an algorithm to find optimal or nearoptimal “stochastic resonance ” (SR) noise benefits for Neyman–Pearson hypothesis testing and for more general inequalityconstrained signal detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequalityconstrained signal detectors. There exists a sequence of noise variables whose detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman–Pearson and other signal detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find nearoptimal noise. The appendix presents the proofs of the main results.
Noise Benefits in QuantizerArray Correlation Detection and Watermark Decoding
"... Abstract—Quantizer noise can improve statistical signal detection in arraybased nonlinear correlators in NeymanPearson and maximumlikelihood (ML) detection. This holds even for infinitevariance symmetric alphastable channel noise and for generalizedGaussian channel noise. Noiseenhanced correla ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
Abstract—Quantizer noise can improve statistical signal detection in arraybased nonlinear correlators in NeymanPearson and maximumlikelihood (ML) detection. This holds even for infinitevariance symmetric alphastable channel noise and for generalizedGaussian channel noise. Noiseenhanced correlation detection leads to noiseenhanced watermark extraction based on such nonlinear detection at the pixel or bit level. This yields a noisebased algorithm for digital watermark decoding using two new noisebenefit theorems. The first theorem gives a necessary and sufficient condition for quantizer noise to increase the detection probability of a constant signal for a fixed falsealarm probability if the channel noise is symmetric and if the sample size is large. The second theorem shows that the array must contain more than one quantizer for such a stochasticresonance noise benefit if the symmetric channel noise is unimodal. It also shows that the noisebenefit
Applications of Forbidden Interval Theorems in Stochastic Resonance
"... Abstract Forbidden interval theorems state whether a stochasticresonance noise benefit occurs based on whether the average noise value falls outside or inside an interval of parameter values. Such theorems act as a type of screening device for mutualinformation noise benefits in the detection of s ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract Forbidden interval theorems state whether a stochasticresonance noise benefit occurs based on whether the average noise value falls outside or inside an interval of parameter values. Such theorems act as a type of screening device for mutualinformation noise benefits in the detection of subthreshold signals. Their proof structure reduces the search for a noise benefit to the often simple task of showing that a zero limit exists. This chapter presents the basic forbidden interval theorem for threshold neurons and four applications of increasing complexity. The first application shows that small amounts of electrical noise can help a carbon nanotube detect faint electrical signals. The second application extends the basic forbidden interval theorem to quantum communication through the judicious use of squeezed light. The third application extends the theorems to noise benefits in standard models of spiking retinas. The fourth application extends the noise benefits in retinal and other neuron models to Levy noise that generalizes Brownian motion and allows for jump and impulsive noise processes. 1 Forbidden Interval Theorems for Stochastic Resonance Stochastic resonance (SR) occurs in a nonlinear system when noise benefits the system [3, 17, 33]. The noise benefit can take the form of an increase in mutual information or a signaltonoise ratio or correlation or a decrease in an error measure. But when will such a noise benefit occur? Forbidden interval theorems answer that SR question for several nonlinear systems. The theorems act as a type of SR screening device because they can give sufficient or necessary conditions for an SR noise benefit. We here restrict noise benefits
Uncorrected Proof
"... Existing methods of complexity research are capable of describing certain specifics of biosystems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimesabrupt state transitions. Chao ..."
Abstract
 Add to MetaCart
Existing methods of complexity research are capable of describing certain specifics of biosystems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimesabrupt state transitions. Chaos tools have the potential of reaching to the essential driving mechanisms that organize matter into living substances. Our basic thesis is that while established chaos tools are useful in describing complexity in physical systems, they lack the power of grasping the essence of the complexity of life. This thesis illustrates sensory perception of vertebrates and the operation of the vertebrate brain. The study of complexity, at the level of biological systems, cannot be completed by the analytical tools, which have been developed for nonliving systems. We propose a new approach to chaos research that has the potential of characterizing biological complexity. Our study is biologically motivated and solidly based in the biodynamics of higher brain function. Our biocomplexity model has the following features, (1) it is highdimensional, but the dimensionality is not rigid, rather it changes dynamically; (2) it is not autonomous and continuously interacts and communicates with individual environments that are selected by the model from the infinitely complex world; (3) as a result, it is adaptive and modifies its internal organization in response to environmental factors by changing them to meet its own goals; (4) it is a distributed object that evolves both in space and time towards goals that is continually reshaping in the light of cumulative experience stored in memory; (5) it is driven and stabilized by noise of internal origin through selforganizing dynamics. The resulti...
IOP PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND THEORETICAL
, 2009
"... Quantum forbiddeninterval theorems for stochastic resonance ..."
Contents lists available at SciVerse ScienceDirect Neural Networks
"... journal homepage: www.elsevier.com/locate/neunet ..."
I. STOCHASTIC RESONANCE IN NEURAL SIGNAL
, 2008
"... Abstract—Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends standard Brownian noise to many types of impulsive jumpnoise processes found in real and model neurons as well as in models of finance and other random phenomena. Two new theorems and the Itô calculus show ..."
Abstract
 Add to MetaCart
Abstract—Levy noise can help neurons detect faint or subthreshold signals. Levy noise extends standard Brownian noise to many types of impulsive jumpnoise processes found in real and model neurons as well as in models of finance and other random phenomena. Two new theorems and the Itô calculus show that white Levy noise will benefit subthreshold neuronal signal detection if the noise process’s scaled drift velocity falls inside an interval that depends on the threshold values. These results generalize earlier ‘forbidden interval ’ theorems of neuronal ‘stochastic resonance ’ or noiseinjection benefits. Global and local Lipschitz conditions imply that additive white Levy noise can increase the mutual information or bit count of several feedback neuron models that obey a general stochastic differential equation. Simulation results show that the same noise benefits still occur for some infinitevariance stable Levy noise processes even though the theorems themselves apply only to finitevariance Levy noise. The Appendix proves the two Itôtheoretic lemmas that underlie the new Levy noisebenefit theorems. Index Terms—Levy noise, Signal detection, Stochastic resonance,