Despite the impressive successes in machine learning, artificial intelligence (AI) still not outperform natural ones. As they do not understand what they learn, they work more inefficiently on cognitive tasks. Today’s machine learning runs on serial von-Neumann computers making the training process inflexible and energy-intensive, whereas the human brain is a massive parallel network of neurons divided into functionally specialized regions participating as a context-dependent, self-organized, and transient subnetwork which is shifted by changes in attention every 0.5 to 2 s. This, however, leads to one of the most puzzling issues in cognitive neuroscience, well known as the binding problem, which is strongly interwoven with an understanding of the correlation of the network dynamics and its connectivity, i.e., the understanding of spatial -temporal patterns. Here, neuronal plasticity is a crucial factor leading to a structural connectivity matrix of synaptic weights that determines the overall function of the network. To pave the way to cognitive electronics this project aims to emulate the formation of temporal-spatial patterns in memristive crossbar arrays and to make them usable for unconventional computing. For this purpose, the effect of thermal crosstalk will be exploited. This should be achieved by tailoring the memristive device characteristics and crossbar structures properties so that they satisfy local learning rules. The experimental investigation will be complemented by developing variability-aware compact models for the fabricated memristive devices and array-level models that include thermal coupling effects between devices in space and time. Based on the simulations and the experimental validation, we want to deduce design rules for memristive crossbar arrays that enable the exploitation of thermal coupling effects in the most efficient way. To ensure optimal training properties for memristive neural networks we want to develop memristive crossbars that implement local learning mechanisms via suitable plasticity mechanisms to generate spatial-temporal patterns and stabilize the system dynamics at the edge of chaos. At this critical state neural networks have maximum computational properties in terms of sensitivity, dynamic range, correlation length, information transfer, and susceptibility. Therefore, a particular goal is to use thermal crosstalk and leakage currents to initiate associative learning mechanisms. The final objective is a neuromorphic system that works with the lowest possible circuit overhead and has a minimum number of input and output neurons.