  Physical reservoir computing is a computational framework that implements
spatiotemporal information processing directly within physical systems. By
exciting nonlinear dynamical systems and creating linear models from their
state, we can create highly energy-efficient devices capable of solving machine
learning tasks without building a modular system consisting of millions of
neurons interconnected by synapses. To act as an effective reservoir, the
chosen dynamical system must have two desirable properties: nonlinearity and
memory. We present task agnostic spatial measures to locally measure both of
these properties and exemplify them for a specific physical reservoir based
upon magnetic skyrmion textures. In contrast to typical reservoir computing
metrics, these metrics can be resolved spatially and in parallel from a single
input signal, allowing for efficient parameter search to design efficient and
high-performance reservoirs. Additionally, we show the natural trade-off
between memory capacity and nonlinearity in our reservoir's behaviour, both
locally and globally. Finally, by balancing the memory and nonlinearity in a
reservoir, we can improve its performance for specific tasks.
