Milano Gianluca, Michieletti Fabio, Pilati Davide, Ricciardi Carlo, Miranda Enrique
Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Torino, Italy.
Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino, Italy.
Nat Commun. 2025 Apr 13;16(1):3509. doi: 10.1038/s41467-025-58741-2.
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 deterministic and stochastic dynamics on computing capabilities of the system in the context of physical reservoir computing. These results pave the way for the development of physical computing paradigms exploiting deterministic and stochastic dynamics in the same hardware platform in a similar way to what our brain does.
神经形态计算旨在开发能够模拟大脑效能的硬件平台。在此背景下，受大脑启发的自组织忆阻网络已被证明是用于材料内计算的有前景的物理基板。然而，理解这些系统中网络动力学与信息处理能力之间的联系仍然是一项挑战。在这项工作中，我们表明神经形态纳米线网络行为可以被建模为一个奥恩斯坦 - 乌伦贝克过程，该过程全面地结合了依赖刺激的确定性轨迹和随机效应。这个统一的建模框架能够描述包括噪声和跳跃在内的网络动力学的主要特征，从而能够在物理水库计算的背景下，研究和量化确定性和随机动力学对系统计算能力所起的作用。这些结果为开发物理计算范式铺平了道路，这种范式以类似于我们大脑的方式在同一硬件平台中利用确定性和随机动力学。
