  Nanowire networks (NWNs) represent a unique hardware platform for
neuromorphic information processing. In addition to exhibiting synapse-like
resistive switching memory at their cross-point junctions, their self-assembly
confers a neural network-like topology to their electrical circuitry, something
that is impossible to achieve through conventional top-down fabrication
approaches. In addition to their low power requirements, cost effectiveness and
efficient interconnects, neuromorphic NWNs are also fault-tolerant and
self-healing. These highly attractive properties can be largely attributed to
their complex network connectivity, which enables a rich repertoire of adaptive
nonlinear dynamics, including edge-of-chaos criticality. Here, we show how the
adaptive dynamics intrinsic to neuromorphic NWNs can be harnessed to achieve
transfer learning. We demonstrate this through simulations of a reservoir
computing implementation in which NWNs perform the well-known benchmarking task
of Mackey-Glass (MG) signal forecasting. First we show how NWNs can predict MG
signals with arbitrary degrees of unpredictability (i.e. chaos). We then show
that NWNs pre-exposed to a MG signal perform better in forecasting than NWNs
without prior experience of an MG signal. This type of transfer learning is
enabled by the network's collective memory of previous states. Overall, their
adaptive signal processing capabilities make neuromorphic NWNs promising
candidates for emerging real-time applications in IoT devices in particular, at
the far edge.
