  Echo state networks are computationally lightweight reservoir models inspired
by the random projections observed in cortical circuitry. As interest in
reservoir computing has grown, networks have become deeper and more intricate.
While these networks are increasingly applied to nontrivial forecasting tasks,
there is a need for comprehensive performance analysis of deep reservoirs. In
this work, we study the influence of partitioning neurons given a budget and
the effect of parallel reservoir pathways across different datasets exhibiting
multi-scale and nonlinear dynamics.
