Effects of Colored Noise on Stochastic Resonance in Sensory Neurons

Nozaki, Daichi; Mar, D. J.; Grigg, Peter; Collins, James J.

doi:10.1103/physrevlett.82.2402

Cited by 274 publications

(135 citation statements)

References 23 publications

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“…A startling discovery that (under certain circumstances) neurons can spike more regularly when stimulated by noise [15][16][17] led to assertions that noise is inherent to neuron function. Several subsequent experimental and theoretical studies were aimed at elucidating the functionality of neural noise [18][19][20][21][22][23]. In the first instance, noise -as expected -introduces a variability in the interspike intervals and degrades the information capacity of the spike trains, with low contrast signals being most affected [22].…”

Section: Introductionmentioning

confidence: 99%

Neuron dynamics in the presence of1/fnoise

Sobie

Babul²,

Sousa³

2011

Phys. Rev. E

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Interest in understanding the interplay between noise and the response of a non-linear device cuts across disciplinary boundaries. It is as relevant for unmasking the dynamics of neurons in noisy environments as it is for designing reliable nanoscale logic circuit elements and sensors. Most studies of noise in non-linear devices are limited to either time-correlated noise with a Lorentzian spectrum (of which the white noise is a limiting case) or just white noise. We use analytical theory and numerical simulations to study the impact of the more ubiquitous "natural" noise with a 1/f frequency spectrum. Specifically, we study the impact of the 1/f noise on a leaky integrate and fire model of a neuron. The impact of noise is considered on two quantities of interest to neuron function: The spike count Fano factor and the speed of neuron response to a small step-like stimulus. For the perfect (non-leaky) integrate and fire model, we show that the Fano factor can be expressed as an integral over noise spectrum weighted by a (low pass) filter function given by F(t, f ) = sinc 2 (πf t). This result elucidates the connection between low frequency noise and disorder in neuron dynamics. Under 1/f noise, spike dynamics lacks a characteristic correlation time, inducing the leaky and non-leaky models to exhibit non-ergodic behavior and Fano factor increasing logarithmically as a function of time. We compare our results to experimental data of single neurons in vivo [M.C. Teich, C. Heneghan, S.B. Lowen, T. Ozaki, and E. Kaplan, Journal of the Optical Society of America A 14, 529 (1997)], and show how the 1/f noise model provides much better agreement than the usual approximations based on Lorentzian noise. The low frequency noise, however, complicates the case for information coding scheme based on interspike intervals by introducing variability in the neuron response time. On a positive note, the neuron response time to a step stimulus is, remarkably, nearly optimal in the presence of 1/f noise. An explanation of this effect elucidates how the brain can take advantage of noise to prime a subset of the neurons to respond almost instantly to sudden stimuli.

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Section: Introductionmentioning

confidence: 99%

Neuron dynamics in the presence of1/fnoise

Sobie

Babul²,

Sousa³

2011

Phys. Rev. E

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“…Here it is to be noted that the noise of biological(non thermal) origin is non Gaussian in character. Recent experimental and theoretical studies in neural network and sensory systems [21,22] offer strong indication that the noise sources in these systems could be non-Gaussian. The noise of biological origin in many cases is due to nonlinear dynamics which may be correlated and non-Gaussian in character, specifically, in the context biological evolution [21,23].…”

Section: Introductionmentioning

confidence: 99%

Kinetics of self-induced aggregation of Brownian particles: Non-Markovian and non-Gaussian features

Ghosh

Sen²,

Bag³

2008

Phys. Rev. E

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In this paper we have studied a model for self-induced aggregation in Brownian particle incorporating the non-Markovian and non-Gaussian character of the associated random noise process. In this model the time evolution of each individual is guided by an over-damped Langevin equation of motion with a non-local drift resulting from the local unbalance distributions of the other individuals. Our simulation result shows that colored nose can induce the cluster formation even at large noise strength. Another observation is that critical noise strength grows very rapidly with increase of noise correlation time for Gaussian noise than non Gaussian one. However, at long time limit the cluster number in aggregation process decreases with time following a power law. The exponent in the power law increases remarkable for switching from Markovian to non Markovian noise process.

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“…The main sources of noise are related to the synaptic connections and voltage-gated channels [1,2,3,4]. The role of noise in the functioning of the non-linear nervous system is poorly understood but there are evidences of positive interactions of noise and nonlinearity in neuronal systems [5,6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Emergence of synchronous oscillations in neural networks excited by noise

Zorzano

Vázquez

2003

Physica D: Nonlinear Phenomena

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The presence of noise in non linear dynamical systems can play a constructive role, increasing the degree of order and coherence or evoking improvements in the performance of the system. An example of this positive influence in a biological system is the impulse transmission in neurons and the synchronization of a neural network. Integrating numerically the Fokker-Planck equation we show a self-induced synchronized oscillation. Such an oscillatory state appears in a neural network coupled with a feedback term, when this system is excited by noise and the noise strength is within a certain range.

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Effects of Colored Noise on Stochastic Resonance in Sensory Neurons

Cited by 274 publications

References 23 publications

Neuron dynamics in the presence of1/fnoise

Neuron dynamics in the presence of1/fnoise

Kinetics of self-induced aggregation of Brownian particles: Non-Markovian and non-Gaussian features

Emergence of synchronous oscillations in neural networks excited by noise

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