Brain-inspired computing with self-assembled networks of nano-objects

Abstract Major efforts to reproduce functionalities and energy efficiency of the brain have been focused on the development of artificial neuromorphic systems based on crossbar arrays of memristive devices fabricated by top-down lithographic technologies. Although very powerful, this approach does not emulate the topology and the emergent behavior of biological neuronal circuits, where the principle of self-organization regulates both structure and function. In materia computing has been proposed as an alternative exploiting the complexity and collective phenomena originating from various classes of physical substrates composed of a large number of non-linear nanoscale junctions. Systems obtained by the self-assembling of nano-objects like nanoparticles and nanowires show spatio-temporal correlations in their electrical activity and functional synaptic connectivity with nonlinear dynamics. The development of design-less networks offers powerful brain-inspired computing capabilities and the possibility of investigating critical dynamics in complex adaptive systems. Here we review and discuss the relevant aspects concerning the fabrication, characterization, modeling, and implementation of networks of nanostructures for data processing and computing applications. Different nanoscale electrical conduction mechanisms and their influence on the meso- and macroscopic functional properties of the systems are considered. Criticality, avalanche effects, edge-of-chaos, emergent behavior, synaptic functionalities are discussed in detail together with applications for unconventional computing. Finally, we discuss the challenges related to the integration of nanostructured networks and with standard microelectronics architectures.