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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
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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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