Journal:

Article Title: Cellular mechanisms of temporal sensitivity in visual cortex neurons

doi: 10.1523/JNEUROSCI.5279-09.2010

Figure Lengend Snippet: Comparison of one-dimensional and two-dimensional receptive field maps. The receptive field of this simple layer 4 regular-spiking cell was first mapped using a Gaussian-filtered noise stimulus, which generated a detailed two-dimensional map (left). The receptive field was then mapped by presenting individual optimally oriented bright and dark bars in 16 positions covering the same area of visual space, generating the corresponding one-dimensional map (right). The two sets of stimulus responses identified the same set of receptive field subregions, indicating good correspondence between the one- and two-dimensional maps.

Article Snippet: We then mapped the receptive field of the cell with a Gaussian filtered noise movie (SD of filter 0.82 pixels;( Niell and Stryker, 2008 ).

Techniques: Comparison, Generated

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