In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks
Open accessAbstract
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.
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Notable results have been recently attained with 'in materia' network implementations.
“However, notable results have been recently attained with ‘in materia’ network implementations (Milano et al., 2022; Pistolesi et al., 2025).”
From Wetware network-based AI: a chemical approach to embodied cognition for robotics and artificial intelligence (2026) · cites this paper as Milano et al., 2022
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Self-citation
- Authors
- Milano, G., Pedretti, G., Montano, K., Ricci, S., Hashemkhani, S., Boarino, L., Ielmini, D., & Ricciardi, C.
- Affiliations
- Istituto Nazionale di Ricerca Metrologica (IT)
- Politecnico di Milano (IT)
- Politecnico di Torino (IT)
- ORCID
- Venue
- Nature Materials
- Published
- 2021-10-04
- Volume
- 21
- Issue
- 2
- Pages
- 195-202
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W3204457379
- Cited by
- 462
- References
- 48
- Citations by year
- 2026: 74 · 2025: 113 · 2024: 116 · 2023: 106 · 2022: 51 · 2021: 2
- FWCI
- 20.18
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- CINECA IRIS Institutional Research Information System (IRIS Istituto Nazionale di Ricerca Metrologica) (submittedVersion, other-oa)
- PORTO@Iris (submittedVersion, other-oa)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Funders
- Compagnia di San Paolo
- European Commission
- European Metrology Programme for Innovation and Research (EMPIR 20FUN06, 20FUN06 MEMQuD, 20FUN06, EMPIR 20FUN06 MEMQuD)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
- Keywords
- reservoir computing, memristors, neuromorphic computing, crossbar arrays, resistive random access memory, spatio-temporal pattern recognition, time series forecasting, synaptic connectivity, nonlinear dynamics, fading memory, complex networks
- MeSH terms
- Brain, Neurons, Neural Networks, Computer, Nonlinear Dynamics, Nanowires
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