where x is a parameter describing the evolution of resistance, I is the current across the device and β β is an effective parameter which characterizes the response of the device to a current flow. If the device decays over time to a high resistance state, one can also add a term − − α α x to the right-hand side of the evolution for x , where α α is a decay constant. However, such resistive switching has been known since the late 60's. The model above is often called Williams-Strukov or Strukov model. Albeit this model is too simplistic to represent real devices, it still serves as a good model exhibiting a pinched hysteresis loop in the current-voltage diagram. However, because of Kirchhoff's laws, the evolution of networks of these components becomes utterly complicated, in particular for disordered neuromorphic materials such as nanowires. Often, these are called memristive networks. The simplest example of a memristive circuit or network is a memristors crossbar. A memristor crossbar is often used as a way to address single memristors for a variety of applications in artificial intelligence. However, this is a one particular example of memristive network arranged on a two dimensional grid. Memristive networks have also important applications, for instance, in reservoir computing. A network of memristors can serve as a reservoir for nonlinearly transforming an input signal into a high-dimensional feature space. The memristor-based reservoir concept was introduced by Kulkarni and Teuscher in 2012. While this model was initially employed for tasks like wave pattern classification and associative memory, the readout mechanism utilized a genetic algorithm, which inherently operates non-linearly. A memristive network is a circuit that satisfies the Kirchhoff laws, e.g. the conservation of the currents at the nodes, and in which every circuit element is a memristive component. Kirchhoff's laws can be written in terms of the sum of the currents on node n as