Tailoring Artificial Neural Networks for Optimal Learning
This work provides insights for researchers in machine learning and related fields by offering a new way to design task-specific ESNs, though it is incremental as it builds on existing ESN paradigms.
The study tackled the unclear impact of reservoir networks on echo state network (ESN) performance by using spectral analysis to identify a key factor affecting memory capacity and showing that adding short loops can tailor ESNs for specific tasks, validated on synthetic and real benchmark time series.
As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing. A key feature of the ESN paradigm is its reservoir --- a directed and weighted network of neurons that projects the input time series into a high dimensional space where linear regression or classification can be applied. Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear. Combining tools from physics, dynamical systems and network science, we attempt to open the black box of ESN and offer insights to understand the behavior of general artificial neural networks. Through spectral analysis of the reservoir network we reveal a key factor that largely determines the ESN memory capacity and hence affects its performance. Moreover, we find that adding short loops to the reservoir network can tailor ESN for specific tasks and optimize learning. We validate our findings by applying ESN to forecast both synthetic and real benchmark time series. Our results provide a new way to design task-specific ESN. More importantly, it demonstrates the power of combining tools from physics, dynamical systems and network science to offer new insights in understanding the mechanisms of general artificial neural networks.