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 realms of "Biologically-inspired Neural Networks", "Shallow Learning" and "Recurrent Neural Networks" in general. "echoes": Machine Learning with Echo State Networks, a scikit-learn compatible package. Code Repository: https://github.com/fabridamicelli/echoes A few interesting features: - scikit-learn compatible, i.e. scikit-learn tools such as GridSearchCV should work out-of-the-box - It is light-weight and fast (`numba` accelerated), i.e. many experiments can be simply run on a laptop - Flexible and customizable, e.g. use arbitrary connectivity, add custom activations, visualize neurons activity, etc. - Installation and getting started is easy: `pip install echoes` - Example notebooks: https://github.com/fabridamicelli/echoes/tree/master/examples/notebooks - Documentation: https://fabridamicelli.github.io/echoes/ As of today, a few people already trust it and the package registers >18K total downloads, ~500/month (pypi.org<http://pypi.org/>) and has been used already in a few research projects: - Hadaeghi et. al. (2021) Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell Ca2+ fluorescence microscopy: www.nature.com/articles/s41598-021-87607-y#Sec4<http://www.nature.com/articles/s41598-021-87607-y#Sec4> - Damicelli et.al<http://et.al/>. (2021) Brain Connectivity meets Reservoir Computing: www.biorxiv.org/content/10.1101/2021.01.22.427750v1.abstract<http://www.biorxiv.org/content/10.1101/2021.01.22.427750v1.abstract> - Fakhar et. al. (2022) Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks: arxiv.org/abs/2205.11947<http://arxiv.org/abs/2205.11947> Check it out and any feedback/suggestions/bug reports are more than welcome (simply open an issue: https://github.com/fabridamicelli/echoes/issues). All the best, Fabrizio