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Generative Manifold Networks (GMN)


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) introduced by Pao et al.

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


Installation

Python Package Index (PyPI) gmn.

pip install gmn


Documentation

GMN documentation.


Usage

Example usage at the python prompt in directory gmn:

>>> import gmn
>>> G = gmn.GMN( configFile = './config/default.cfg' )
>>> G.Generate()
>>> G.DataOut.tail()
     Time             A         B       C         D       Out
295   996 -2.487000e-01  0.927389 -0.5018  0.383759 -0.902106
296   997 -1.874000e-01  0.973968 -0.4708  0.471114 -0.961839
297   998 -1.253000e-01  0.989932 -0.4248  0.540129 -0.989022
298   999 -6.280002e-02  0.984369 -0.3671  0.591274 -0.986631
299  1000 -2.438686e-08  0.957464 -0.3016  0.624011 -0.951023

References

Experimentally testable whole brain manifolds that recapitulate behavior

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