I would like to point you to a potentially useful Python Package that might be of interest for your research, especially in the realm dynamical models and spreading dynamics on networks.

"ser": S(usceptible)E(xcited)R(efractory) model on graphs, a dynamical model of spreading excitations.

Code Repository: https://github.com/fabridamicelli/ser

As of today, a few people already trust it and the package registers >11K total downloads, ~500/month (pypi.org) and has been used already in a few research projects.

A few interesting features:

 - It is light-weight and fast (`numba` accelerated), i.e. many experiments can be simply run on a laptop

 - Installation and getting started is easy: `pip install ser`

 - Example notebooks: https://github.com/fabridamicelli/ser/blob/main/examples

The SER model is a classical cellular automaton model used to study all sorts of complex systems, ranging from forest fire dynamics to self-organized criticality in the brain.

Despite of the simplicity of its setup, it is capable of generating complex emergent dynamics with absolutely non-trivial higher-order statistical properties.

To learn more about the SER model and its applications, you might want to check out these references:

  - Messe et al. (2018). Toward a theory of coactivation patterns in excitable neural networks. doi.org/10.1371/journal.pcbi.1006084

  - Haimovici et al. (2013). Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome. doi.org/10.1103/PhysRevLett.110.178101

  - Damicelli et al. (2019). Topological reinforcement as a principle of modularity emergence in brain networks. doi.org/10.1162/netn_a_00085

Check it out and any feedback/suggestions/bug reports are more than welcome (simply open an issue: https://github.com/fabridamicelli/ser/issues).

All the best,

Fabrizio Damicelli