  Networks of nanowires are currently being explored for a range of
applications in brain-like (or neuromorphic) computing, and especially in
reservoir computing (RC). Fabrication of real-world computing devices requires
that the nanowires are deposited sequentially, leading to stacking of the wires
on top of each other. However, most simulations of computational tasks using
these systems treat the nanowires as 1D objects lying in a perfectly 2D plane -
the effect of stacking on RC performance has not yet been established. Here we
use detailed simulations to compare the performance of perfectly 2D and
quasi-3D (stacked) networks of nanowires in two tasks: memory capacity and
nonlinear transformation. We also show that our model of the junctions between
nanowires is general enough to describe a wide range of memristive networks,
and consider the impact of physically realistic electrode configurations on
performance. We show that the various networks and configurations have a
strikingly similar performance in RC tasks, which is surprising given their
radically different topologies. Our results show that networks with an
experimentally achievable number of electrodes perform close to the upper
bounds achievable when using the information from every wire. However, we also
show important differences, in particular that the quasi-3D networks are more
resilient to changes in the input parameters, generalizing better to noisy
training data. Since previous literature suggests that topology plays an
important role in computing performance, these results may have important
implications for future applications of nanowire networks in neuromorphic
computing.
