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custom bvp solver  (MathWorks Inc)


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    MathWorks Inc custom bvp solver
    Novel analytical theory of correlated colored noise-induced synchronization of heterogeneous oscillators matches Monte Carlo simulations for low to moderate levels of <t>noise.</t> <t>Stationary</t> phase difference density is shown as computed from the solution of the <t>BVP</t> and through Monte Carlo simulation from t = 1000 to t = 201000 in steps of 0.05. Monte Carlo data binned into 100 bins between −π and π. There is a frequency difference of ϵ 2 /2 where ϵ is the magnitude of the noise. Here Δ j (θ j ) = sin( a j ) − sin(θ j + a j ) + b j sin(2θ j ), where j = 1, 2 for two oscillators. (A) τ = 1, a 1 = 0.1, a 2 = 0.6, b 1 = 0.32, b 2 = 0.3, and c = 0.8. (B) τ = 0.25, a 1 = a 2 = 0.5, b 1 = b 2 = 0.3, and c = 0.5.
    Custom Bvp Solver, supplied by MathWorks 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/custom bvp solver/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    custom bvp solver - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Impact of neuronal heterogeneity on correlated colored noise-induced synchronization"

    Article Title: Impact of neuronal heterogeneity on correlated colored noise-induced synchronization

    Journal: Frontiers in Computational Neuroscience

    doi: 10.3389/fncom.2013.00113

    Novel analytical theory of correlated colored noise-induced synchronization of heterogeneous oscillators matches Monte Carlo simulations for low to moderate levels of noise. Stationary phase difference density is shown as computed from the solution of the BVP and through Monte Carlo simulation from t = 1000 to t = 201000 in steps of 0.05. Monte Carlo data binned into 100 bins between −π and π. There is a frequency difference of ϵ 2 /2 where ϵ is the magnitude of the noise. Here Δ j (θ j ) = sin( a j ) − sin(θ j + a j ) + b j sin(2θ j ), where j = 1, 2 for two oscillators. (A) τ = 1, a 1 = 0.1, a 2 = 0.6, b 1 = 0.32, b 2 = 0.3, and c = 0.8. (B) τ = 0.25, a 1 = a 2 = 0.5, b 1 = b 2 = 0.3, and c = 0.5.
    Figure Legend Snippet: Novel analytical theory of correlated colored noise-induced synchronization of heterogeneous oscillators matches Monte Carlo simulations for low to moderate levels of noise. Stationary phase difference density is shown as computed from the solution of the BVP and through Monte Carlo simulation from t = 1000 to t = 201000 in steps of 0.05. Monte Carlo data binned into 100 bins between −π and π. There is a frequency difference of ϵ 2 /2 where ϵ is the magnitude of the noise. Here Δ j (θ j ) = sin( a j ) − sin(θ j + a j ) + b j sin(2θ j ), where j = 1, 2 for two oscillators. (A) τ = 1, a 1 = 0.1, a 2 = 0.6, b 1 = 0.32, b 2 = 0.3, and c = 0.8. (B) τ = 0.25, a 1 = a 2 = 0.5, b 1 = b 2 = 0.3, and c = 0.5.

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    custom bvp solver  (MathWorks Inc)
    90
    MathWorks Inc custom bvp solver
    Novel analytical theory of correlated colored noise-induced synchronization of heterogeneous oscillators matches Monte Carlo simulations for low to moderate levels of <t>noise.</t> <t>Stationary</t> phase difference density is shown as computed from the solution of the <t>BVP</t> and through Monte Carlo simulation from t = 1000 to t = 201000 in steps of 0.05. Monte Carlo data binned into 100 bins between −π and π. There is a frequency difference of ϵ 2 /2 where ϵ is the magnitude of the noise. Here Δ j (θ j ) = sin( a j ) − sin(θ j + a j ) + b j sin(2θ j ), where j = 1, 2 for two oscillators. (A) τ = 1, a 1 = 0.1, a 2 = 0.6, b 1 = 0.32, b 2 = 0.3, and c = 0.8. (B) τ = 0.25, a 1 = a 2 = 0.5, b 1 = b 2 = 0.3, and c = 0.5.
    Custom Bvp Solver, supplied by MathWorks 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/custom bvp solver/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    custom bvp solver - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

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    Novel analytical theory of correlated colored noise-induced synchronization of heterogeneous oscillators matches Monte Carlo simulations for low to moderate levels of noise. Stationary phase difference density is shown as computed from the solution of the BVP and through Monte Carlo simulation from t = 1000 to t = 201000 in steps of 0.05. Monte Carlo data binned into 100 bins between −π and π. There is a frequency difference of ϵ 2 /2 where ϵ is the magnitude of the noise. Here Δ j (θ j ) = sin( a j ) − sin(θ j + a j ) + b j sin(2θ j ), where j = 1, 2 for two oscillators. (A) τ = 1, a 1 = 0.1, a 2 = 0.6, b 1 = 0.32, b 2 = 0.3, and c = 0.8. (B) τ = 0.25, a 1 = a 2 = 0.5, b 1 = b 2 = 0.3, and c = 0.5.

    Journal: Frontiers in Computational Neuroscience

    Article Title: Impact of neuronal heterogeneity on correlated colored noise-induced synchronization

    doi: 10.3389/fncom.2013.00113

    Figure Lengend Snippet: Novel analytical theory of correlated colored noise-induced synchronization of heterogeneous oscillators matches Monte Carlo simulations for low to moderate levels of noise. Stationary phase difference density is shown as computed from the solution of the BVP and through Monte Carlo simulation from t = 1000 to t = 201000 in steps of 0.05. Monte Carlo data binned into 100 bins between −π and π. There is a frequency difference of ϵ 2 /2 where ϵ is the magnitude of the noise. Here Δ j (θ j ) = sin( a j ) − sin(θ j + a j ) + b j sin(2θ j ), where j = 1, 2 for two oscillators. (A) τ = 1, a 1 = 0.1, a 2 = 0.6, b 1 = 0.32, b 2 = 0.3, and c = 0.8. (B) τ = 0.25, a 1 = a 2 = 0.5, b 1 = b 2 = 0.3, and c = 0.5.

    Article Snippet: We solve the BVP for the stationary phase difference density using a custom BVP solver written in MATLAB.

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