  The Deep Time-Delay Reservoir Computing concept utilizes unidirectionally
connected systems with time-delays for supervised learning. We present how the
dynamical properties of a deep Ikeda-based reservoir are related to its memory
capacity (MC) and how that can be used for optimization. In particular, we
analyze bifurcations of the corresponding autonomous system and compute
conditional Lyapunov exponents, which measure the generalized synchronization
between the input and the layer dynamics. We show how the MC is related to the
systems distance to bifurcations or magnitude of the conditional Lyapunov
exponent. The interplay of different dynamical regimes leads to a adjustable
distribution between linear and nonlinear MC. Furthermore, numerical
simulations show resonances between clock cycle and delays of the layers in all
degrees of the MC. Contrary to MC losses in a single-layer reservoirs, these
resonances can boost separate degrees of the MC and can be used, e.g., to
design a system with maximum linear MC. Accordingly, we present two
configurations that empower either high nonlinear MC or long time linear MC.
