Document Zbl 1302.68235

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Sci. Rev.</a> <a href="/?q=in%3A336844" title="Articles in this Issue">3, No. 3, 127-149 (2009)</a>. </div> <div class="abstract">Summary: Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both current ways of generating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current “brand-names” of reservoir methods, and thus aims to help in unifying the field and providing the reader with a detailed “map” of it.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A6352113">Cited in <strong>135</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A68T05" title="MSC2020">68T05</a> </td> <td class="space"> Learning and adaptive systems in artificial intelligence </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A92B20" title="MSC2020">92B20</a> </td> <td class="space"> Neural networks for/in biological studies, artificial life and related topics </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A68-02" title="MSC2020">68-02</a> </td> <td class="space"> Research exposition (monographs, survey articles) pertaining to computer science </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Arecurrent+neural+networks">recurrent neural networks</a>; <a href="/?q=ut%3Areservoir+computing">reservoir computing</a></div> <div class="software"> <h3>Software:</h3><a href="/software/36450">Evolino</a>; <a href="/software/11086">darch</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;">  </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function">  <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 1302.68235" data-ciurl="/ci/06352113" data-biburl="/bibtex/06352113.bib" data-amsurl="/amsrefs/06352113.bib" data-xmlurl="/xml/06352113.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/06352113.pdf" title="Zbl 1302.68235 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1016/j.cosrev.2009.03.005" aria-label="DOI for “Reservoir computing approaches to recurrent neural network training”" title="10.1016/j.cosrev.2009.03.005">DOI</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Hopfield, John J., Hopfield network, Scholarpedia, 2, 5, 1977 (2007)</td> </tr><tr> <td>[2]</td> <td class="space">Hopfield, John J., Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the United States of America, 79, 2554-2558 (1982) · <a href="/1369.92007" class="nowrap">Zbl 1369.92007</a></td> </tr><tr> <td>[3]</td> <td class="space">Hinton, Geoffrey E., Boltzmann machine, Scholarpedia, 2, 5, 1668 (2007)</td> </tr><tr> <td>[4]</td> <td class="space">Ackley, David H.; Hinton, Geoffrey E.; Sejnowski, Terrence J., A learning algorithm for Boltzmann machines, Cognitive Science, 9, 147-169 (1985)</td> </tr><tr> <td>[5]</td> <td class="space">Hinton, Geoffrey E.; Salakhutdinov, Ruslan, Reducing the dimensionality of data with neural networks, Science, 313, 5786, 504-507 (2006) · <a href="/1226.68083" class="nowrap">Zbl 1226.68083</a></td> </tr><tr> <td>[6]</td> <td class="space">Taylor, Graham W.; Hinton, Geoffrey E.; Roweis, Sam, Modeling human motion using binary latent variables, (Advances in Neural Information Processing Systems 19. 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