Self-organizing neuromorphic nanowire networks are stochastic dynamical systems

Neuromorphic computing aims to develop software and hardware platforms emulating the information processing effectiveness of our brain. In this context, self-organizing neuromorphic nanonetworks have been demonstrated as suitable physical substrates for in materia implementation of unconventional computing paradigms, like reservoir computing. However, understanding the relationship between emergent dynamics and information processing capabilities still represents a challenge. Here, we demonstrate that nanowire-based neuromorphic networks are stochastic dynamical systems where the signals flow relies on the intertwined action of deterministic and random factors. We show through an experimental and modeling approach that these systems combine stimuli-dependent deterministic trajectories and random effects caused by noise and jumps that can be holistically described by an Ornstein-Uhlenbeck process, providing a unifying framework surpassing current modeling approaches of self-organizing neuromorphic nanonetworks (not only nanowire-based) that are limited to either deterministic or stochastic effects. Since information processing capabilities can be dynamically tuned by controlling the network’s attractor memory state, these results open new perspectives for the rational development of physical computing paradigms exploiting deterministic and stochastic dynamics in a single hardware platform similarly to our brain.