Memristor

Engineering

Statistics

Realization (probability)

Materials science

Telecommunications

Computer science

Distributed computing

Reservoir computing

Nanowire

Nanotechnology

Computer architecture

Crossbar switch

Mathematics

Neuromorphic engineering

Electronic engineering

Artificial intelligence

Topology (electrical circuits)

Artificial neural network

Recurrent neural network

Electrical engineering

Nature Materials

Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost. A network of self-organized nanowires combined with a memristive read-out layer is used to demonstrate a hardware implementation of reservoir computing for recognition of spatio-temporal patterns and time-series prediction.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks