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) see: Park et al.
GMN is developed at the Biological Nonlinear Dynamics Data Science Unit, OIST
Installation
Python Package Index (PyPI) gmn.
pip install gmn
Documentation
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
References
Experimentally testable whole brain manifolds that recapitulate behavior
Explainable prediction and simulation of complex system dynamics through networks of manifolds
1 maintainer
Metadata
1 maintainer
Release files for gmn 2.0.1
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