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Nonlinear Behavior of Dendritic Polymer Networks for Reservoir Computing
Advanced Electronic Materials ( IF 5.3 ) Pub Date : 2021-08-16 , DOI: 10.1002/aelm.202100330 Lautaro Petrauskas 1 , Matteo Cucchi 2 , Christopher Grüner 2 , Frank Ellinger 1 , Karl Leo 2 , Christian Matthus 1 , Hans Kleemann 2
Advanced Electronic Materials ( IF 5.3 ) Pub Date : 2021-08-16 , DOI: 10.1002/aelm.202100330 Lautaro Petrauskas 1 , Matteo Cucchi 2 , Christopher Grüner 2 , Frank Ellinger 1 , Karl Leo 2 , Christian Matthus 1 , Hans Kleemann 2
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Organic electrochemical devices are an emerging class of devices with synaptic properties that might allow for the implementation of next-generation neuromorphic circuits for power-efficient computing. Here, a brain-inspired neural network approach, namely reservoir computing, which relies on a nonlinear transformation of a low-dimensional input signal onto a high-dimensional output space for information processing is utilized. The implementation of reservoir computing using dendritic networks of polymeric fibers is demonstrated and the nonlinear response of the polymer networks are analyzed and the sources of nonlinearity are identified. Furthermore, by adding a delayed feedback loop to the reservoir, it is proven that such a network can undergo a bifurcation into a chaotic state, proving sufficient complexity of the system for advanced classification tasks with time-dependent data. Ultimately, a classification task is carried out and the accuracy is compared of the classification of different degrees of complexity of the system, showing an increase in accuracy from 60% for the base network to 80% when the delayed feedback loop is incorporated.
中文翻译：
用于油藏计算的树枝状聚合物网络的非线性行为
有机电化学器件是一类新兴的具有突触特性的器件，可能允许实现下一代神经形态电路以进行节能计算。在这里，使用了一种受大脑启发的神经网络方法，即水库计算，它依赖于低维输入信号到高维输出空间的非线性变换来进行信息处理。演示了使用聚合物纤维的树枝状网络进行储层计算，分析了聚合物网络的非线性响应，并确定了非线性的来源。此外，通过向水库添加延迟反馈回路，证明了这样的网络可以经历分叉进入混沌状态，证明具有时间相关数据的高级分类任务的系统足够复杂。最终，进行了分类任务，并比较了系统不同复杂程度的分类准确率，显示在加入延迟反馈回路后，准确率从基础网络的 60% 提高到 80%。
更新日期：2021-08-16
中文翻译：
用于油藏计算的树枝状聚合物网络的非线性行为
有机电化学器件是一类新兴的具有突触特性的器件，可能允许实现下一代神经形态电路以进行节能计算。在这里，使用了一种受大脑启发的神经网络方法，即水库计算，它依赖于低维输入信号到高维输出空间的非线性变换来进行信息处理。演示了使用聚合物纤维的树枝状网络进行储层计算，分析了聚合物网络的非线性响应，并确定了非线性的来源。此外，通过向水库添加延迟反馈回路，证明了这样的网络可以经历分叉进入混沌状态，证明具有时间相关数据的高级分类任务的系统足够复杂。最终，进行了分类任务，并比较了系统不同复杂程度的分类准确率，显示在加入延迟反馈回路后，准确率从基础网络的 60% 提高到 80%。
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