Self-organizing neuromorphic nanowire networks as stochastic dynamical systems.

ABSTRACT: Neuromorphic computing aims to develop hardware platforms that emulate the effectiveness of our brain. In this context, brain-inspired self-organizing memristive networks have been demonstrated as promising physical substrates for in materia computing. However, understanding the connection between network dynamics and information processing capabilities in these systems still represents a challenge. In this work, we show that neuromorphic nanowire network behavior can be modeled as an Ornstein-Uhlenbeck process which holistically combines stimuli-dependent deterministic trajectories and stochastic effects. This unified modeling framework, able to describe main features of network dynamics including noise and jumps, enables the investigation and quantification of the roles played by determi

SUBMITTER: Milano G 

PROVIDER: S-EPMC11994789 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature