Self-organizing neuromorphic nanowire networks as stochastic dynamical systems DOI Creative Commons
Gianluca Milano, Fabio Michieletti,

Davide Pilati

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 13, 2025

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 connection between network dynamics and information processing capabilities these systems still represents a challenge. work, we show neuromorphic nanowire behavior can be modeled an Ornstein-Uhlenbeck process which holistically combines stimuli-dependent deterministic trajectories stochastic effects. This unified modeling framework, able describe main features including noise jumps, enables investigation quantification roles played by on system context reservoir These results pave way development paradigms exploiting same platform similar what brain does.

Language: Английский

Self‐organized Criticality in Neuromorphic Nanowire Networks With Tunable and Local Dynamics DOI Creative Commons
Fabio Michieletti,

Davide Pilati,

Gianluca Milano

et al.

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

Abstract Self‐organized criticality (SOC) has attracted large interest as a key property for the optimization of information processing in biological neural systems. Inspired by this synergy, nanoscale self‐organizing devices are demonstrated to emulate critical dynamics due their complex nature, proving be ideal candidates hardware implementation brain‐inspired unconventional computing paradigms. However, controlling emerging and understanding its relationship with capabilities remains challenge. Here, it is shown that memristive nanowire networks (NWNs) can programmed state through appropriate electrical stimulation. Furthermore, multiterminal characterization reveals network areas establish spatial interactions endowing local dynamics. The impact such tunable versus experimentally analyzed materia nonlinear transformation (NLT) tasks, framework reservoir computing. As brain where cortical specialized certain function, performance rely on response reduced subsets outputs, which may show or not, depending specificity task. Such brain‐like behavior lead neuromorphic systems based complexity exploiting behavior.

Language: Английский

Citations

1
Self-organizing neuromorphic nanowire networks as stochastic dynamical systems DOI Creative Commons
Gianluca Milano, Fabio Michieletti,

Davide Pilati

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 13, 2025

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 connection between network dynamics and information processing capabilities these systems still represents a challenge. work, we show neuromorphic nanowire behavior can be modeled an Ornstein-Uhlenbeck process which holistically combines stimuli-dependent deterministic trajectories stochastic effects. This unified modeling framework, able describe main features including noise jumps, enables investigation quantification roles played by on system context reservoir These results pave way development paradigms exploiting same platform similar what brain does.

Language: Английский

Citations

0