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Memristive synapses with high reproducibility for flexible neuromorphic networks based on biological nanocomposites

机译:记忆性突触高重现性灵活的基于神经网络生物纳米复合材料

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Memristive synapses from biomaterials are promising for building flexible and implantable artificial neuromorphic systems due to their remarkable mechanical and biological properties. However, these biological devices have relatively poor memristive switching characteristics, and thus fail to meet the requirement of neuromorphic networks for high learning accuracy. Here, memristive synapses based on carrageenan nanocomposites that possess desirable characteristics are demonstrated. These devices show highly reproducible analog resistive switching behaviors with 250 conductance states, low write noise, good write linearity, high retention of more than 10(4) s and endurance for at least 10(6) pulses. The enhanced switching properties are attributed to controllable and confined conductive filament growth, owing to the synergistic effect of self-assembled silver nanocluster doping and nanocone-shaped electrode contact. Moreover, the devices exhibit excellent reliability after 1000 bending cycles. Simulations including the non-ideal factors prove that the synaptic device array can operate with an online learning accuracy of 94.3%. These findings enable broader applications of biomaterials in flexible memristive devices and neuromorphic systems.
机译:记忆性突触的生物材料承诺用于构建灵活和植入人工神经系统由于他们卓越的机械和生物属性。然而,这些生物设备相对可怜的记忆性转换特征,因此无法满足神经形态的要求网络学习精度高。基于角叉菜胶的记忆性突触纳米复合材料,具有理想的特征。显示高度可再生的模拟电阻交换行为与250年电导州,低噪音,写好写线性,高保留超过10(4)年代和耐力至少10(6)脉冲。属性是归因于可控在导电长丝增长,由于协同效应的自组装银纳米掺杂和nanocone-shaped电极接触。1000年之后弯曲周期可靠性。模拟包括证明的非理想因素突触设备阵列可以操作一个在线学习精度为94.3%。研究结果支持更广泛的应用生物材料在灵活的记忆性设备和神经形态系统。

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