Abstract：
:Physical reservoir computing approaches have gained increased attention in recent years due to their potential for low-energy high-performance computing. Despite recent successes, there are bounds to what one can achieve simply by making physical reservoirs larger. Therefore, we argue that a switch from single-reservoir computing to multi-reservoir and even deep physical reservoir computing is desirable. Given that error backpropagation cannot be used directly to train a large class of multi-reservoir systems, we propose an alternative framework that combines the power of backpropagation with the speed and simplicity of classic training algorithms. In this work we report our findings on a conducted experiment to evaluate the general feasibility of our approach. We train a network of 3 Echo State Networks to perform the well-known NARMA-10 task, where we use intermediate targets derived through backpropagation. Our results indicate that our proposed method is well-suited to train multi-reservoir systems in an efficient way.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Freiberger M,Bienstman P,Dambre Jdoi
10.1038/s41598-020-71549-ysubject
Has Abstractpub_date
2020-09-02 00:00:00pages
14451issue
1issn
2045-2322pii
10.1038/s41598-020-71549-yjournal_volume
10pub_type
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