gaussian noise Search Results


gaussian noise  (GraphPad Software Inc)
90
GraphPad Software Inc gaussian noise
Gaussian Noise, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gaussian noise/product/GraphPad Software Inc
Average 90 stars, based on 1 article reviews
gaussian noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
signal detection in non-gaussian noise  (Verlag GmbH)
90
Verlag GmbH signal detection in non-gaussian noise
Signal Detection In Non Gaussian Noise, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/signal detection in non-gaussian noise/product/Verlag GmbH
Average 90 stars, based on 1 article reviews
signal detection in non-gaussian noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
additive white gaussian noise  (SR Research)
90
SR Research additive white gaussian noise
Input signal with <t>AWGN</t> 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).
Additive White Gaussian Noise, supplied by SR Research, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/additive white gaussian noise/product/SR Research
Average 90 stars, based on 1 article reviews
additive white gaussian noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
gaussian unbiased random noise  (Baier labs)
90
Baier labs gaussian unbiased random noise
Input signal with <t>AWGN</t> 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).
Gaussian Unbiased Random Noise, supplied by Baier labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gaussian unbiased random noise/product/Baier labs
Average 90 stars, based on 1 article reviews
gaussian unbiased random noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
white gaussian noise  (Ziemer USA Inc)
90
Ziemer USA Inc white gaussian noise
Input signal with <t>AWGN</t> 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).
White Gaussian Noise, supplied by Ziemer USA Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/white gaussian noise/product/Ziemer USA Inc
Average 90 stars, based on 1 article reviews
white gaussian noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
gaussian white noise  (Rocha labs)
90
Rocha labs gaussian white noise
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 <t>Gaussian</t> 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).
Gaussian White Noise, supplied by Rocha labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gaussian white noise/product/Rocha labs
Average 90 stars, based on 1 article reviews
gaussian white noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
gaussian noise  (Corning Life Sciences)
90
Corning Life Sciences gaussian noise
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 <t>Gaussian</t> 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).
Gaussian Noise, supplied by Corning Life Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gaussian noise/product/Corning Life Sciences
Average 90 stars, based on 1 article reviews
gaussian noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
colored gaussian noise  (Spacek Labs)
90
Spacek Labs colored gaussian noise
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 <t>Gaussian</t> 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).
Colored Gaussian Noise, supplied by Spacek Labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/colored gaussian noise/product/Spacek Labs
Average 90 stars, based on 1 article reviews
colored gaussian noise - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
gaussian noise wgn  (Bell Telephone Laboratories)
90
Bell Telephone Laboratories gaussian noise wgn
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 <t>Gaussian</t> 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).
Gaussian Noise Wgn, supplied by Bell Telephone Laboratories, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gaussian noise wgn/product/Bell Telephone Laboratories
Average 90 stars, based on 1 article reviews
gaussian noise wgn - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
gaussian noise generators  (Maanshan Tianjun Machinery Manufacturing Co LTD)
90
Maanshan Tianjun Machinery Manufacturing Co LTD gaussian noise generators
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 <t>Gaussian</t> 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).
Gaussian Noise Generators, supplied by Maanshan Tianjun Machinery Manufacturing Co LTD, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gaussian noise generators/product/Maanshan Tianjun Machinery Manufacturing Co LTD
Average 90 stars, based on 1 article reviews
gaussian noise generators - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
b. gaussian self-affinity and fractals: globality, the earth, 1/f noise, and r/s  (Verlag GmbH)
90
Verlag GmbH b. gaussian self-affinity and fractals: globality, the earth, 1/f noise, and r/s
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 <t>Gaussian</t> 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).
B. Gaussian Self Affinity And Fractals: Globality, The Earth, 1/F Noise, And R/S, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/b. gaussian self-affinity and fractals: globality, the earth, 1/f noise, and r/s/product/Verlag GmbH
Average 90 stars, based on 1 article reviews
b. gaussian self-affinity and fractals: globality, the earth, 1/f noise, and r/s - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
igor pro gaussian noise function  (wavemetrics inc)
90
wavemetrics inc igor pro gaussian noise function
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 <t>Gaussian</t> 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).
Igor Pro Gaussian Noise Function, supplied by wavemetrics inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/igor pro gaussian noise function/product/wavemetrics inc
Average 90 stars, based on 1 article reviews
igor pro gaussian noise function - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier

Image Search Results


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).

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:

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).

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:

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.

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

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).

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