  Neuro-inspired recurrent neural network algorithms, such as echo state
networks, are computationally lightweight and thereby map well onto untethered
devices. The baseline echo state network algorithms are shown to be efficient
in solving small-scale spatio-temporal problems. However, they underperform for
complex tasks that are characterized by multi-scale structures. In this
research, an intrinsic plasticity-infused modular deep echo state network
architecture is proposed to solve complex and multiple timescale temporal
tasks. It outperforms state-of-the-art for time series prediction tasks.
