使用具有可调动力学和短期可塑性的忆阻器的类脑储备池计算
Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity
AI总结：
针对储备池计算中相同忆阻器导致状态空间不足的问题，提出利用电压或离子通道浓度调控离子通道忆阻器动力学以构建多样化储备池，实验表明该方法在系统预测和神经活动分类任务中显著提升了准确率并降低了误差。
摘要：
储备池计算研究的最新进展对具有动力学的模拟器件产生了需求，这些动力学能够促进储备池的物理实现，有望实现更快的信息处理，同时消耗更少的能量并占用更小的面积。研究表明，具有非线性和短期记忆动力学的动态忆阻器是作为信息处理器件或用于时序分类和预测任务的储备池的极佳候选者。以前的实现依赖于名义上相同的忆阻器，对输入数据应用相同的非线性变换，这不足以实现丰富的状态空间。为了解决这一限制，研究人员要么在多个忆阻器中实现数据编码的多样化，要么利用忆阻器之间的随机器件间变异性。然而，这种方法需要额外的预处理步骤并导致同步问题。相反，更优选的做法是对数据进行一次编码，然后将其通过由具有不同动力学的忆阻器组成的储备池层。在这里，我们证明了具有电压依赖性动力学的基于离子通道的忆阻器可以通过电压或调节离子通道浓度来可控且可预测地调谐，从而表现出多样的动态特性。我们通过实验和模拟表明，由少量不同的忆阻器构建的储备池层在单次数据编码下表现出显著更高的预测和分类准确率。我们发现，对于二阶非线性动力系统预测任务，多样化的忆阻器储备池在实验上仅使用五个不同的忆阻器就实现了0.0015的归一化均方误差。此外，在神经活动分类任务中，仅由三个不同的忆阻器组成的储备池在实验上达到了96.5%的准确率。
英文摘要：
Recent advancements in reservoir computing research have created a demand for analog devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional pre-processing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass it through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved a normalized mean square error of 0.0015 using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%.
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