Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910411563991040 |
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| author | Baccetti, Valentina Zhu, Ruomin Kuncic, Zdenka Caravelli, Francesco |
| author_facet | Baccetti, Valentina Zhu, Ruomin Kuncic, Zdenka Caravelli, Francesco |
| contents | Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices. In this study, we explore the connection between ergodicity in memristive and nanowire networks, showing that the performance of reservoir devices improves when these networks are tuned to operate at the edge between two global stability points. The lack of ergodicity is associated with the emergence of memory in the system. We measure the level of ergodicity using the Thirumalai-Mountain metric, and we show that in the absence of ergodicity, two memristive systems show improved performance when utilized as reservoir computers (RC). In particular, we highlight that it is also important to let the system synchronize to the input signal in order for the performance of the RC to exhibit improvements over the baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09530 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks Baccetti, Valentina Zhu, Ruomin Kuncic, Zdenka Caravelli, Francesco Disordered Systems and Neural Networks Statistical Mechanics Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices. In this study, we explore the connection between ergodicity in memristive and nanowire networks, showing that the performance of reservoir devices improves when these networks are tuned to operate at the edge between two global stability points. The lack of ergodicity is associated with the emergence of memory in the system. We measure the level of ergodicity using the Thirumalai-Mountain metric, and we show that in the absence of ergodicity, two memristive systems show improved performance when utilized as reservoir computers (RC). In particular, we highlight that it is also important to let the system synchronize to the input signal in order for the performance of the RC to exhibit improvements over the baseline. |
| title | Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks |
| topic | Disordered Systems and Neural Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2310.09530 |