Dear Computational Neuroscience Community, 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