Generative Manifold Networks (GMN)

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Generative Manifold Networks is a generalization of nonlinear dynamical systems from a single state-space with a manifold operator, to an interconnected network of operators on the state-space(s) see: Park et al.

GMN is developed at the Biological Nonlinear Dynamics Data Science Unit, OIST

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Installation

Python Package Index (PyPI) gmn.

pip install gmn

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Documentation

GMN documentation.

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Usage

Example usage at the python prompt in directory gmn/config:

>>> import gmn
>>> G = gmn.GMN( configFile = './default.cfg' )
>>> G.Generate()
>>> G.DataOut.tail()
     Time       A       C       D         B       Out
295   996 -0.2487 -0.5018  0.7500  0.985236 -0.979370
296   997 -0.1874 -0.4708  0.7937  0.985842 -0.991504
297   998 -0.1253 -0.4248  0.8177  0.965066 -0.973041
298   999 -0.0628 -0.3671  0.8224  0.923630 -0.931681
299  1000  0.0000 -0.3016  0.8090  0.862222 -0.871642

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References

Experimentally testable whole brain manifolds that recapitulate behavior

Explainable prediction and simulation of complex system dynamics through networks of manifolds