Neural information processing and time-series prediction with only two dynamical memristors
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arXiv
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| Format: | Preprint |
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2023
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| author | Molnár, Dániel Török, Tímea Nóra Volk Jr., János Kövecs, Roland Pósa, László Balázs, Péter Molnár, György Olalla, Nadia Jimenez Balogh, Zoltán Volk, János Leuthold, Juerg Csontos, Miklós Halbritter, András |
| author_facet | Molnár, Dániel Török, Tímea Nóra Volk Jr., János Kövecs, Roland Pósa, László Balázs, Péter Molnár, György Olalla, Nadia Jimenez Balogh, Zoltán Volk, János Leuthold, Juerg Csontos, Miklós Halbritter, András |
| contents | Memristive devices are commonly benchmarked by the multi-level programmability of their resistance states. Neural networks utilizing memristor crossbar arrays as synaptic layers largely rely on this feature. However, the dynamical properties of memristors, such as the adaptive response times arising from the exponential voltage dependence of the resistive switching speed remain largely unexploited. Here, we propose an information processing scheme which fundamentally relies on the latter. We realize simple dynamical memristor circuits capable of complex temporal information processing tasks. We demonstrate an artificial neural circuit with one nonvolatile and one volatile memristor which can detect a neural spike pattern in a very noisy environment, fire a single voltage pulse upon successful detection and reset itself in an entirely autonomous manner. Furthermore, we implement a circuit with only two nonvolatile memristors which can learn the operation of an external dynamical system and perform the corresponding time-series prediction with high accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_13320 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neural information processing and time-series prediction with only two dynamical memristors Molnár, Dániel Török, Tímea Nóra Volk Jr., János Kövecs, Roland Pósa, László Balázs, Péter Molnár, György Olalla, Nadia Jimenez Balogh, Zoltán Volk, János Leuthold, Juerg Csontos, Miklós Halbritter, András Mesoscale and Nanoscale Physics Other Condensed Matter Memristive devices are commonly benchmarked by the multi-level programmability of their resistance states. Neural networks utilizing memristor crossbar arrays as synaptic layers largely rely on this feature. However, the dynamical properties of memristors, such as the adaptive response times arising from the exponential voltage dependence of the resistive switching speed remain largely unexploited. Here, we propose an information processing scheme which fundamentally relies on the latter. We realize simple dynamical memristor circuits capable of complex temporal information processing tasks. We demonstrate an artificial neural circuit with one nonvolatile and one volatile memristor which can detect a neural spike pattern in a very noisy environment, fire a single voltage pulse upon successful detection and reset itself in an entirely autonomous manner. Furthermore, we implement a circuit with only two nonvolatile memristors which can learn the operation of an external dynamical system and perform the corresponding time-series prediction with high accuracy. |
| title | Neural information processing and time-series prediction with only two dynamical memristors |
| topic | Mesoscale and Nanoscale Physics Other Condensed Matter |
| url | https://arxiv.org/abs/2307.13320 |