Journal: Sensors (Basel, Switzerland)

Article Title: A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

doi: 10.3390/s23021022

Figure Lengend Snippet: Input signal with AWGN of different D ( a ) time domain ( D = 0.5), ( b ) frequency domain ( D = 0.5), ( c ) time domain ( D = 0.7), ( d ) frequency domain ( D = 0.7), ( e ) time domain ( D = 0.9), ( f ) frequency domain ( D = 0.9), ( g ) time domain ( D = 1.1), ( h ) frequency domain ( D = 1.1).

Article Snippet: Additive white Gaussian noise (AWGN) is often used as a driving source in SR research due to its convenience in numerical simulation and uniform distribution, but as a special noise, it is of great significance to study the SR principle of dichotomous noise as a driving source for nonlinear dynamics.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

doi: 10.3390/s23021022

Figure Lengend Snippet: Output signal with AWGN of different D ( a ) time domain ( D = 0.5), ( b ) frequency domain ( D = 0.5), ( c ) time domain ( D = 0.7), ( d ) frequency domain ( D = 0.7), ( e ) time domain ( D = 0.9), ( f ) frequency domain ( D = 0.9), ( g ) time domain ( D = 1.1), ( h ) frequency domain ( D = 1.1).

Article Snippet: Additive white Gaussian noise (AWGN) is often used as a driving source in SR research due to its convenience in numerical simulation and uniform distribution, but as a special noise, it is of great significance to study the SR principle of dichotomous noise as a driving source for nonlinear dynamics.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

doi: 10.3390/s23021022

Figure Lengend Snippet: Comparison of high-value D ( a ) input time spectrum with dichotomous noise, ( b ) input frequency spectrum with dichotomous noise, ( c ) output time spectrum with dichotomous noise, ( d ) output frequency spectrum with dichotomous noise, ( e ) input time spectrum with AWGN, ( f ) input frequency spectrum with AWGN, ( g ) output time spectrum with AWGN, ( h ) output frequency spectrum with AWGN.

Article Snippet: Additive white Gaussian noise (AWGN) is often used as a driving source in SR research due to its convenience in numerical simulation and uniform distribution, but as a special noise, it is of great significance to study the SR principle of dichotomous noise as a driving source for nonlinear dynamics.

Techniques: Comparison

Journal: The Journal of Neuroscience

Article Title: Role of Input Correlations in Shaping the Variability and Noise Correlations of Evoked Activity in the Neocortex

doi: 10.1523/JNEUROSCI.4536-14.2015

Figure Lengend Snippet: Model of evoked activity with two input sources. a, 3D representation of the network activity as it shifts from ongoing state (black circle) to the evoked state (green filled circle). Green empty circle represents stimulus statistics. Dashed red line indicates direction of the jump. Large red arrow indicates jump magnitude (Φ). Small red arrows indicate magnitude of the transformation projected onto the respective axis (φρ, φν, φCV). b, Schematic diagram illustrating the concept of two different input sources: feedback and feedforward. Each source has its own event train statistics (νm, ρb, CVm2) and within-correlation structure (Nw, ρw, f(ξ)). c, Two LIF neurons (represented as in Fig. 1c) each receiving two independent currents with Gaussian statistics (black and gray traces). Gray traces represent shared currents ζci where i = 1,2 denotes source index. Black traces represent independent currents ζji, where j = 1,2 is the neuron index and i as before. d–f, White markers represent results from simulations. Solid traces represent analytical approximation. d, Correlation susceptibility φρ as a function of the variance ratio γ. Black/green trace represents φρ associated with ongoing/stimulus input source. e, Effect of γ on the output correlations (ρout) for different values of ρb1 and a fixed value of ρb2 = 0.2. Green arrow indicates direction of γ increase. f, Effect of γ on νout (black) and CVout2 (gray).

Article Snippet: In the previous literature, a common approach has been to simplify the input model, reducing it to Gaussian white noise, which is defined by only two parameters: mean (μ) and SD (σ) of the Gaussian distribution ( de la Rocha et al., 2007 ; Moreno-Bote et al., 2008 ; Hong et al., 2012 ; Schultze-Kraft et al., 2013 ).

Techniques: Activity Assay, Transformation Assay

Journal: Frontiers in Neuroscience

Article Title: Finite Post Synaptic Potentials Cause a Fast Neuronal Response

doi: 10.3389/fnins.2011.00019

Figure Lengend Snippet: Background shapes equilibrium density and response . (A) Probability density of voltage V of a perfect integrator driven by Gaussian white noise (2). (B) Probability density of a perfect integrator driven by excitatory synaptic impulses of finite size w causing the same drift and fluctuations as in (A) given by (3). The green curve shows the collective histogram of a direct simulation of a population of 20,000 model neurons with random initial conditions observed for 1 s (bin size (V θ − V r )/100 same as line width of black curve). The density near threshold most strongly differs on the scale of the synaptic amplitude w (gray shaded region). (C) An additional excitatory impulse of amplitude s shifts the density (here for Gaussian white noise background input), so that the gray shaded area exceeds the threshold. (D) The probability P inst. to respond with an action potential corresponds to the area of density above threshold in (C) . P inst. depends on the shape of the density near threshold and hence on the type of background input (black: background of synaptic impulses of size w given by (5), gray: Gaussian white noise background (4). Further parameters used for this and all other figures are specified in Section .

Article Snippet: Since a neuron receives many synaptic afferents each having only a small impact, a common approach is to replace the total synaptic input by a Gaussian white noise in the so called diffusion approximation: only the mean μ and the variance σ 2 of the total input are kept (Siegert, ; Johannesma, ; Ricciardi and Sacerdote, ; Lánský, ; Risken, ).

Techniques: