Nowadays,memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses,making them become a new generation of research hotspots for brain-like neurocomputing.In th...Nowadays,memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses,making them become a new generation of research hotspots for brain-like neurocomputing.In this work,we prepare a memristor based on two-dimensionalα-In_(2)Se_(3)nanosheets,which exhibits excellent electrical properties,faster switching speeds,and continuous tunability of device conduction.Meanwhile,most basic bio-synapse functions can be implemented faithfully,such as short-term memory(STM),long-term memory(LTM),four different types of spike-timing-dependent plasticity(STDP),and paired-pulse facilitation(PPF).More importantly,we systematically study three effective methods to achieve LTM,in which the reinforcement learning can be faithfully simulated according to the Ebbinghaus forgetting curve.Therefore,we believe this work will promote the development of learning functions for brain-like computing and artificial intelligence.展开更多
基金supported by the National Key R&D Plan“Nano Frontier”Key Special Project(2021YFA1200502)the National Natural Science Foundation of China(62004056,61874158,and 62104058)+12 种基金the Cultivation Projects of National Major R&D Project(92164109)the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences(XDB44000000-7)Hebei Basic Research Special Key Project(F2021201045)the Support Program for the Top Young Talents of Hebei Province(70280011807)the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province(SLRC2019018)the Interdisciplinary Research Program of Natural Science of Hebei University(DXK202101)the Institute of Life Sciences and Green Development(521100311)the Natural Science Foundation of Hebei Province(F2022201054 and F2021201022)the Outstanding Young Scientific Research and Innovation Team of Hebei University(605020521001)the Special Support Funds for National High Level Talents(041500120001)the Advanced Talents Incubation Program of the Hebei University(521000981426,521100221071,and 521000981363)the Science and Technology Project of Hebei Education Department(QN2020178 and QN2021026)Baoding Science and Technology Plan Project(2172P011)。
基金financially supported by the National Key R&D Plan“Nano Frontier”Key Special Project(2021YFA1200502)the Cultivation Projects of National Major R&D Project(92164109)+9 种基金the National Natural Science Foundation of China(61874158,62004056 and 62104058)the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences(XDB44000000-7)Hebei Basic Research Special Key Project(F2021201045)the Support Program for the Top Young Talents of Hebei Province(70280011807)the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province(SLRC2019018)the Outstanding Young Scientific Research and Innovation Team of Hebei University(605020521001)the Special Support Funds for National High Level Talents(041500120001)the High-level Talent Research Startup Project of Hebei University(521000981426)the Science and Technology Project of Hebei Education Department(QN2020178 and QN2021026)the Post-graduate’s Innovation Fund Project of Hebei Province(CXZZBS2022020)。
文摘Nowadays,memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses,making them become a new generation of research hotspots for brain-like neurocomputing.In this work,we prepare a memristor based on two-dimensionalα-In_(2)Se_(3)nanosheets,which exhibits excellent electrical properties,faster switching speeds,and continuous tunability of device conduction.Meanwhile,most basic bio-synapse functions can be implemented faithfully,such as short-term memory(STM),long-term memory(LTM),four different types of spike-timing-dependent plasticity(STDP),and paired-pulse facilitation(PPF).More importantly,we systematically study three effective methods to achieve LTM,in which the reinforcement learning can be faithfully simulated according to the Ebbinghaus forgetting curve.Therefore,we believe this work will promote the development of learning functions for brain-like computing and artificial intelligence.
