Dear Community, I'd like to announce the new release (version 1.0.0) of the Python Package "echoes": Machine Learning with Echo State Networks, a scikit-learn compatible package. Code Repository: https://github.com/fabridamicelli/echoes Especially in the realms of "Biologically-inspired Neural Networks", "Shallow Learning" and "Recurrent Neural Networks" in general. A few interesting features:     - Installation and getting started is easy: `pip install echoes` * API follows scikit-learn standard (eg fit/predict).     - It is light-weight and fast (`numba` accelerated), run your experiments on a laptop     - Flexible and customizable, e.g. use arbitrary connectivity, add custom activations, visualize neurons activity, etc. * Example notebooks: https://github.com/fabridamicelli/echoes/tree/master/examples/notebooks     - Documentation: https://fabridamicelli.github.io/echoes/ As of today the package registers already ~40K total downloads, ~600/month (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 - Fakhar et. al. (2022) Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks: arxiv.org/abs/2205.11947 - Damicelli 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> 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