PyDDM - A generalized drift diffusion model simulatorΒΆ

PyDDM is a simulator and modeling framework for generalized drift-diffusion models (GDDM or DDM), with a focus on cognitive neuroscience.

Key features include:

  • Fast solutions to generalized drift-diffusion models, allowing data-fitting with a large number of parameters
  • Fokker-Planck equation solved numerically using Crank-Nicolson and backward Euler methods for likelihood fitting on the full distribution
  • Arbitrary functions for parameters drift rate, noise, bounds, and initial position distribution
  • Arbitrary loss function and fitting method for parameter fitting
  • Multiprocessor support
  • Optional GUI for debugging and gaining an intuition for different models
  • Convenient and extensible object oriented API allows building models in a component-wise fashion
  • Verified accuracy of simulations using software verification techniques

Start with the tutorial. To see what PyDDM is capable of, and for example models, see the PyDDM Cookbook. Also see the FAQs for more information. You can also try an interactive online demo on Google Colab.

Release annoucments are posted on the pyddm-announce mailing list and on github.

Please note that PyDDM is still beta software so you may experience some glitches or uninformative error messages. Please report any problems to the bug tracker.