A two-module, feedforward neural network architecture called mapper-coach network has been introduced to reconstruct an unobserved, continuous latent variable input, driving two observed dynamical systems. The method has been demonstrated on time series generated by two chaotic logistic maps driven by a hidden third one. The network has been trained to predict one of the observed time series based on its own past and on the other observed time series by error-back propagation. It was shown, that after this prediction have been learned successfully, the activity of the bottleneck neuron, connecting the mapper and the coach module, correlates strongly with the latent common input variable. The method has the potential to reveal hidden components of dynamical systems, where experimental intervention is not possible.

中文翻译：

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使用mapper-coach网络和误差反向传播重构时间序列中的公共潜在输入

已经引入了一种称为映射器-教练网络的两模块前馈神经网络体系结构，以重构一个未观察到的连续潜变量输入，从而驱动两个观察到的动力学系统。该方法已在由隐藏的第三个图驱动的两个混沌逻辑图生成的时间序列上得到了证明。网络已经过训练，可以根据自身的过去以及通过误差反向传播而根据另一个观察到的时间序列来预测观察到的时间序列之一。结果表明，成功学习了这种预测之后，连接映射器和教练模块的瓶颈神经元的活动与潜在的公共输入变量密切相关。该方法有可能揭示动力学系统中无法进行实验干预的隐藏组件。