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We strongly support open access science. These programs are all provided as free software and you are welcome to re-use this code (see the individual files for license information). If the code is useful we do appreciate a citation of the relevant paper. The code hosted here has been beta tested and we appreciate your feedback and any suggestions to improve it. Please feel free to contact us if you're interested in particular methods that are not listed here. We also support the Wikiproject Neuroscience and try to keep relevant Wikipedia articles up to date. See for instance Neural oscillation.

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Consistency-based thresholding Threshold a group of networks by the consistency of edge weights across the group. Roberts JA, Perry A, Roberts G, Mitchell PB, Breakspear M (2016). Consistency-based thresholding of the human connectome. NeuroImage (in press). 
distob Distributed computing made easier, using automatic remote objects in python. TBA 2015 
Diversity SIRS model Susceptible-infected-refractory-susceptible model with diversity in the excitability parameter. Gollo, L. L., Copelli, M., & Roberts, J. A. (2016). Diversity improves performance in excitable networks. PeerJ, 4, e1912. 
Geometric surrogate networks Generate random surrogate weighted networks that preserve the effect of distance on the weights. Roberts JA, Perry A, Lord AR, Roberts G, Mitchell PB, Smith RE, Calamante F, Breakspear M (2016). The contribution of geometry to the human connectome. NeuroImage 124: 379-393. 
NormalForm Mathematica package: find a smooth transformation that maps a complicated dynamical system to a simple one Aburn, Holmes, Daffertshofer and Breakspear "Normal form transformations explain effect of noise near Hopf bifurcations" in prep. 
nsim Simulate systems from ODEs or SDEs, analyze EEG or other timeseries, all done in parallel on a cluster or multiple CPUs. Aburn, Holmes, Daffertshofer and Breakspear "Normal form transformations explain effect of noise near Hopf bifurcations" in prep. 
sdeint Numerical integration of Ito or Stratonovich SDEs. Includes Euler-Maruyama, Stratonovich Heun and Stochastic Runge-Kutta methods TBA 2015 
SDE integration tools Integrate scalar or vector Stochastic Differential Equations (SDE) in Stratonovich form using the Heun algorithm. Includes example code simulating generalized Ornstein-Uhlenbeck processes (with multiplicative and additive noise) and a simple cortical model (Jansen-Rit). Aburn, Holmes, Roberts, Boonstra and Breakspear (2012) "Critical Fluctuations in Cortical Models Near Instability", Front. Physio. 3:331. doi:10.3389/fphys.2012.00331 
SDMs examples Examples (Morris-Lecar neuron, Breakspear-Terry-Friston neural mass model, and bistable Hopf oscillator) from SDMs paper Roberts JA, Friston KJ, Breakspear M (2016). Clinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer. (submitted) 
time-frequency coherency computes the complex-valued time-frequency coherency between two signal vectors Mehrkanoon S, Breakspear M, Daffertshofer A, Boonstra TW (2013). Non-identical smoothing operators for estimating time-frequency interdependence in electrophysiological recordings. EURASIP Journal on Advances in Signal Processing 2013, 2013:73. doi:10.1186/1687-6180-2013-73 
Showing 10 items
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SDE_integration_tools-20121022.zip
(30k)
Matthew Aburn,
Oct 21, 2012, 9:24 PM
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SDMs_examples.zip
(20k)
James Roberts,
Nov 15, 2016, 5:49 PM
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SIRSdiversity.zip
(3k)
Leonardo Gollo,
Feb 17, 2016, 11:09 PM
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consistency.zip
(2k)
James Roberts,
Mar 15, 2016, 11:30 PM
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geomsurr.zip
(3k)
James Roberts,
Jan 3, 2016, 7:27 PM
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tfcohf.zip
(8k)
Tjeerd B,
Apr 11, 2013, 3:22 AM
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