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:

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