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Dear Community,</div>
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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.</div>
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Code Repository: https://github.com/fabridamicelli/echoes</div>
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Especially in the realms of "Biologically-inspired Neural Networks", "Shallow Learning" and "Recurrent Neural Networks" in general.</div>
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A few interesting features:</div>
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- Installation and getting started is easy: `pip install echoes`</div>
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<div class="elementToProof">API follows scikit-learn standard (eg fit/predict).</div>
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- It is light-weight and fast (`numba` accelerated), run your experiments on a laptop</div>
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- Flexible and customizable, e.g. use arbitrary connectivity, add custom activations, visualize neurons activity, etc.</div>
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<div class="elementToProof">Example notebooks: https://github.com/fabridamicelli/echoes/tree/master/examples/notebooks</div>
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- Documentation: https://fabridamicelli.github.io/echoes/</div>
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As of today the package registers already ~40K total downloads, ~600/month (pypi.org) and has been used already in a few research projects:</div>
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- 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</div>
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- Fakhar et. al. (2022) Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks: arxiv.org/abs/2205.11947</div>
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- Damicelli et.al. (2021) Brain Connectivity meets Reservoir Computing: <a href="http://www.biorxiv.org/content/10.1101/2021.01.22.427750v1.abstract" id="LPlnk575569">
www.biorxiv.org/content/10.1101/2021.01.22.427750v1.abstract</a></div>
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Check it out and any feedback/suggestions/bug reports are more than welcome (simply open an issue: https://github.com/fabridamicelli/echoes/issues).</div>
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All the best,</div>
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Fabrizio</div>
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