Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V. & Pernice, W. H. P. The rise of intelligent matter. Nature 594, 345–355 (2021).
Laydevant, J., Wright, L. G., Wang, T. & McMahon, P. L. The hardware is the software. Neuron 112, 180–183 (2024).
Stieg, A. Z. et al. Emergent criticality in complex turing b-type atomic switch networks. Adv. Mater. 24, 286–293 (2011).
Avizienis, A. V. et al. Morphological transitions from dendrites to nanowires in the electroless deposition of silver. Cryst. Growth Des. 13, 465–469 (2013).
Demis, E. C. et al. Atomic switch networks — nanoarchitectonic design of a complex system for natural computing. Nanotechnology 26, 204003 (2015).
Kim, T. H. et al. Nanoparticle assemblies as memristors. Nano Lett. 9, 2229–2233 (2009).
Sandouk, E. J., Gimzewski, J. K. & Stieg, A. Z. Multistate resistive switching in silver nanoparticle films. Sci. Technol. Adv. Mater. 16, 045004 (2015).
Mallinson, J. B. et al. Avalanches and criticality in self-organized nanoscale networks. Sci. Adv. https://doi.org/10.1126/sciadv.aaw8438 (2019).
Stieg, A. Z. et al. in Memristor Networks (eds Adamatzky, A. & Chua, L.) https://doi.org/10.1007/978-3-319-02630-5_10 (Springer, 2014).
Milano, G., Michieletti, F., Pilati, D., Ricciardi, C. & Miranda, E. Self-organizing neuromorphic nanowire networks as stochastic dynamical systems. Nat. Commun. 16, 3509 (2025).
Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017).
Lynn, C. W. & Bassett, D. S. The physics of brain network structure, function and control. Nat. Rev. Phys. 1, 318–332 (2019).
Diaz-Alvarez, A. et al. Emergent dynamics of neuromorphic nanowire networks. Sci. Rep. https://doi.org/10.1038/s41598-019-51330-6 (2019).
Loeffler, A. et al. Topological properties of neuromorphic nanowire networks. Front. Neurosci. https://doi.org/10.3389/fnins.2020.00184 (2020).
Suárez, L. E., Richards, B. A., Lajoie, G. & Misic, B. Learning function from structure in neuromorphic networks. Nat. Mach. Intell. 3, 771–786 (2021).
Milano, G., Miranda, E. & Ricciardi, C. Connectome of memristive nanowire networks through graph theory. Neural Netw. 150, 137–148 (2022).
Vahl, A., Milano, G., Kuncic, Z., Brown, S. A. & Milani, P. Brain-inspired computing with self-assembled networks of nano-objects. J. Phys. D 57, 503001 (2024).
Zhu, R. et al. Information dynamics in neuromorphic nanowire networks. Sci. Rep. 11, 13047 (2021).
Anderson, P. W. More is different: broken symmetry and the nature of the hierarchical structure of science. Science 177, 393–396 (1972).
Terasa, M.-I. et al. Pathways towards truly brain-like computing primitives. Mater. Today 69, 41–53 (2023).
Chialvo, D. & Bak, P. Learning from mistakes. Neuroscience 90, 1137–1148 (1999).
Carbajal, J. P., Dambre, J., Hermans, M. & Schrauwen, B. Memristor models for machine learning. Neural Comput. 27, 725–747 (2015).
Carbajal, J. P., Martin, D. A. & Chialvo, D. R. Learning by mistakes in memristor networks. Phys. Rev. E https://doi.org/10.1103/physreve.105.054306 (2022).
Barrows, F., Sheldon, F. C. & Caravelli, F. Network analysis of memristive device circuits: dynamics, stability and correlations. J. Phys. Complex. 6, 025003 (2025).
Zhou, Z. et al. Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107, 1738–1762 (2019).
Bourzac, K. Fixing AI’s energy crisis. Nature https://doi.org/10.1038/d41586-024-03408-z (2024).
Milano, G., Porro, S., Valov, I. & Ricciardi, C. Recent developments and perspectives for memristive devices based on metal oxide nanowires. Adv. Electron. Mater. 5, 1800909 (2019).
Kuncic, Z. & Nakayama, T. Neuromorphic nanowire networks: principles, progress and future prospects for neuro-inspired information processing. Adv. Phys. X https://doi.org/10.1080/23746149.2021.1894234 (2021).
Manning, H. G. et al. Emergence of winner-takes-all connectivity paths in random nanowire networks. Nat. Commun. 9, 3219 (2018).
Milano, G. et al. Brain-inspired structural plasticity through reweighting and rewiring in multi-terminal self-organizing memristive nanowire networks. Adv. Intell. Syst. 2, 2000096 (2020).
Sillin, H. O. et al. A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing. Nanotechnology 24, 384004 (2013).
Li, Q. et al. Dynamic electrical pathway tuning in neuromorphic nanowire networks. Adv. Funct. Mater. 30, 2003679 (2020).
Lilak, S. et al. Spoken digit classification by in-materio reservoir computing with neuromorphic atomic switch networks. Front. Nanotechnol. https://doi.org/10.3389/fnano.2021.675792 (2021).
Zhu, R. et al. Online dynamical learning and sequence memory with neuromorphic nanowire networks. Nat. Commun. 14, 6697 (2023).
Kotooka, T. et al. Thermally stable Ag2Se nanowire network as an effective in-materio physical reservoir computing device. Adv. Electron. Mater. 10, 2400443 (2024).
Milano, G. et al. Electrochemical rewiring through quantum conductance effects in single metallic memristive nanowires. Nanoscale Horiz. 9, 416–426 (2024).
Valov, I. et al. Nanobatteries in redox-based resistive switches require extension of memristor theory. Nat. Commun. 4, 1771 (2013).
Valov, I., Waser, R., Jameson, J. & Kozicki, M. Electrochemical metallization memories — fundamentals, applications, prospects. Nanotechnology 22, 254003 (2011).
Yang, Y. et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 4232 (2014).
Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).
Sporns, O. The human connectome: origins and challenges. Neuroimage 80, 53–61 (2013).
Pilati, D., Michieletti, F., Cultrera, A., Ricciardi, C. & Milano, G. Emerging spatiotemporal dynamics in multiterminal neuromorphic nanowire networks through conductance matrices and voltage maps. Adv. Electron. Mater. 10, 2400750 (2024).
Sattar, A., Fostner, S. & Brown, S. A. Quantized conductance and switching in percolating nanoparticle films. Phys. Rev. Lett. 111, 136808 (2013).
Bose, S. K., Mallinson, J. B., Gazoni, R. M. & Brown, S. A. Stable self-assembled atomic-switch networks for neuromorphic applications. IEEE Trans. Electron Devices 64, 5194–5201 (2017).
Shirai, S. et al. Long-range temporal correlations in scale-free neuromorphic networks. Netw. Neurosci. 4, 432 (2020).
Bones, P. J. et al. Comparison of the properties of two-dimensional and three-dimensional percolating networks of nanoparticles. Phys. Rev. E https://doi.org/10.1103/638g-tr91 (2025).
Le, P. Y. et al. Electroformed, self-connected tin oxide nanoparticle networks for electronic reservoir computing. Adv. Electron. Mater. https://doi.org/10.1002/aelm.202000081 (2020).
Mallinson, J. B. et al. Experimental demonstration of reservoir computing with self-assembled percolating networks of nanoparticles. Adv. Mater. https://doi.org/10.1002/adma.202402319 (2024).
Schulze, M. et al. Electrical measurements of nanoscale bismuth cluster films. Eur. Phys. J. D 24, 291 (2003).
Minnai, C., Bellacicca, A., Brown, S. A. & Milani, P. Facile fabrication of complex networks of memristive devices. Sci. Rep. https://doi.org/10.1038/s41598-017-08244-y (2017).
Minnai, C., Mirigliano, M., Brown, S. & Milani, P. The nanocoherer: an electrically and mechanically resettable memristor based on gold clusters assembled on paper. Nano Futures 2, 011002 (2018).
Bose, S. et al. Neuromorphic behaviour in discontinuous metal films. Nanoscale Horiz. 7, 437 (2022).
Profumo, F. et al. Highly efficient classification of time-series based on resistive switching cluster-assembled materials. Adv. Intell. Syst. https://doi.org/10.1002/aisy.202401150 (2025).
Carstens, N. et al. Brain-like critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix. Nanoscale Adv. https://doi.org/10.1039/d2na00121g (2022).
Rao, T. S., Mondal, I., Bannur, B. & Kulkarni, G. U. A scalable solution recipe for a Ag-based neuromorphic device. Discov. Nano https://doi.org/10.1186/s11671-023-03906-5 (2023).
Gronenberg, O. et al. In situ imaging of dynamic current paths in a neuromorphic nanoparticle network with critical spiking behavior. Adv. Funct. Mater. https://doi.org/10.1002/adfm.202312989 (2024).
Pal, M. et al. Device area invariant conductance linearity in scalable silver nanostructure based neuromorphic devices with threshold activation following nociceptive behavior. Nanoscale 17, 19434–19446 (2025).
Pal, M., Vidhyadhiraja, N. S. & Kulkarni, G. U. Realising bio-synapse analogous paired-pulse facilitation (PPF) and release inactivation (RI) and emulating ‘tip of the tongue’ experience through conductance and retention dynamics in ag nano-labyrinth-based neuromorphic device. Adv. Funct. Mater. https://doi.org/10.1002/adfm.202425635 (2025).
Adejube, B. et al. Silver-based self-organized resistive switching nanoparticle networks with neural-like spiking behavior: implications for neuromorphic computing. ACS Appl. Nano Mater. https://doi.org/10.1021/acsanm.5c02520 (2025).
van der Ree, A. J. T., Ahmadi, M., Ten Brink, G. H., Kooi, B. J. & Palasantzas, G. Neuromorphic-like dynamics in percolating copper nanoparticle networks: synthesis, characterization, and limitations. Phys. Rev. Mater. https://doi.org/10.1103/physrevmaterials.9.036001 (2025).
van der Ree, A. J. T., Ahmadi, M., Ten Brink, G. H., Kooi, B. J. & Palasantzas, G. Stable millivolt range resistive switching in percolating molybdenum nanoparticle networks. ACS Appl. Mater. Interfaces 16, 65157–65164 (2024).
Olsen, M., Hummelgård, M. & Olin, H. Surface modifications by field induced diffusion. PLoS One 7, e30106 (2012).
Mallinson, J. et al. Reservoir computing using networks of memristors: effects of topology and heterogeneity. Nanoscale 15, 9663–9674 (2023).
Pike, M. D. et al. Atomic scale dynamics drive brain-like avalanches in percolating nanostructured networks. Nano Lett. 20, 3935 (2020).
Acharya, S. K. et al. Stochastic spiking behaviour in neuromorphic devices enables true random number generation. ACS Appl. Mater. Interfaces 13, 52861 (2021).
Srinivasa, N., Stepp, N. D. & Cruz-Albrecht, J. Criticality as a set-point for adaptive behavior in neuromorphic hardware. Front. Neurosci. https://doi.org/10.3389/fnins.2015.00449 (2015).
Muñoz, M. A. Colloquium: criticality and dynamical scaling in living systems. Rev. Mod. Phys. https://doi.org/10.1103/revmodphys.90.031001 (2018).
Mallinson, J. B. & Brown, S. A. Time-multiplexed reservoir computing with percolating networks of nanoparticles. In Proc. International Joint Conference on Neural Networks https://doi.org/10.1109/IJCNN54540.2023.10191253 (IEEE, 2023).
Heywood, Z., Mallinson, J., Bones, P. & Brown, S. From ‘follow the leader’ to autonomous swarming: physical reservoir computing in two dimensions. Neuromorphic Comput. Eng. 4, 034011 (2024).
Studholme, S. J. et al. Computation via neuron-like spiking in percolating networks of nanoparticles. Nano Lett. 23, 10594 (2023).
Studholme, S. J. & Brown, S. A. Integer factorization with stochastic spiking in percolating networks of nanoparticles. ACS Nano https://doi.org/10.1021/acsnano.4c07200 (2024).
Studholme, S. J., Mallinson, J. B., Steel, J. K. & Brown, S. A. Probabilistic computing with percolating nanoparticle networks using experimental data with signatures of criticality. Neuromorphic Comput. Eng. https://doi.org/10.1088/2634-4386/adc0b8 (2025).
Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).
Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).
Kuncic, Z. et al. Neuromorphic information processing with nanowire networks. In Proc. 2020 IEEE International Symposium on Circuits and Systems https://doi.org/10.1109/ISCAS45731.2020.9181034 (IEEE, 2020).
Montano, K., Milano, G. & Ricciardi, C. Grid–graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks. Neuromorphic Comput. Eng. 2, 014007 (2022).
Diaz-Alvarez, A. et al. Emergent dynamics of neuromorphic nanowire networks. Sci. Rep. 9, 14920 (2019).
Loeffler, A. et al. Neuromorphic learning, working memory, and metaplasticity in nanowire networks. Sci. Adv. 9, eadg3289 (2023).
Daniels, R. K. et al. Brain-like networks of nanowires and nanoparticles: a change of perspective. Phys. Rev. Appl. 20, 034021 (2023).
Daniels, R. K. et al. Reservoir computing with 3D nanowire networks. Neural Netw. 154, 122 (2022).
Monaghan, B. et al. Learning and spiking dynamics in brain-like nanoscale networks. Nanoscale Horiz. 10, d5nh00407a (2025).
Heywood, Z., Monaghan, B., Mallinson, J. & Brown, S. Reservoir computing with networks of nanoscale memristors: optimisation of memristor responses for maximal computational performance. Nanoscale 17, D5NR02474A (2025).
Milano, G., Cultrera, A., Boarino, L., Callegaro, L. & Ricciardi, C. Tomography of memory engrams in self-organizing nanowire connectomes. Nat. Commun. 14, 5723 (2023).
Srikimkaew, O., Azhari, S., Banerjee, D., Usami, Y. & Tanaka, H. Short-term and long-term memory functionality of a brain-like device built from nanoparticle atomic switch networks. Adv. Electron. Mater. https://doi.org/10.1002/aelm.202400360 (2024).
Wilting, J. & Priesemann, V. 25 years of criticality in neuroscience — established results, open controversies, novel concepts. Curr. Opin. Neurobiol. 58, 105–111 (2019).
Hochstetter, J. et al. Avalanches and edge-of-chaos learning in neuromorphic nanowire networks. Nat. Commun. https://doi.org/10.1038/s41467-021-24260-z (2021).
Dunham, C. S. et al. Nanoscale neuromorphic networks and criticality: a perspective. J. Phys. Complex. 2, 042001 (2021).
Fairfield, J. A., Rocha, C. G., O’Callaghan, C., Ferreira, M. S. & Boland, J. J. Co-percolation to tune conductive behaviour in dynamical metallic nanowire networks. Nanoscale 8, 18516–18523 (2016).
Diaz Schneider, J. I., Quinteros, C. P., Levy, P. & Martínez, E. D. Two-junction model in different percolation regimes of silver nanowires networks. Adv. Funct. Mater. https://doi.org/10.1002/adfm.202410766 (2024).
Shekhawat, A., Papanikolaou, S., Zapperi, S. & Sethna, J. P. Dielectric breakdown and avalanches at nonequilibrium metal-insulator transitions. Phys. Rev. Lett. https://doi.org/10.1103/physrevlett.107.276401 (2011).
Sheldon, F. C. & Di Ventra, M. Conducting-insulating transition in adiabatic memristive networks. Phys. Rev. E https://doi.org/10.1103/physreve.95.012305 (2017).
Michieletti, F., Pilati, D., Milano, G. & Ricciardi, C. Self-organized criticality in neuromorphic nanowire networks with tunable and local dynamics. Adv. Funct. Mater. 35, 2423903 (2025).
Sun, H., Radicchi, F., Kurths, J. & Bianconi, G. The dynamic nature of percolation on networks with triadic interactions. Nat. Commun. https://doi.org/10.1038/s41467-023-37019-5 (2023).
Caravelli, F., Bardoscia, M. & Caccioli, F. Emergence of giant strongly connected components in continuum disk-spin percolation. J. Stat. Mech. Theory Exp. 2016, 053211 (2016).
Fostner, S., Brown, R., Carr, J. & Brown, S. A. Continuum percolation with tunneling. Phys. Rev. B 89, 075402 (2014).
Scharnhorst, K. S. et al. Atomic switch networks as complex adaptive systems. Jpn. J. Appl. Phys. 57, 03ED02 (2018).
Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167–11177 (2003).
Hengen, K. B. & Shew, W. L. Is criticality a unified setpoint of brain function? Neuron 113, 2582–2598.e2 (2025).
Sheldon, F. C., Kolchinsky, A. & Caravelli, F. Computational capacity of of LRC, memristive, and hybrid reservoirs. Phys. Rev. E https://doi.org/10.1103/physreve.106.045310 (2022).
Baccetti, V., Zhu, R., Kuncic, Z. & Caravelli, F. Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks. Nano Express 5, 015021 (2024).
Barrows, F., Lin, J., Caravelli, F. & Chialvo, D. R. Uncontrolled learning: Codesign of neuromorphic hardware topology for neuromorphic algorithms. Adv. Intell. Syst. https://doi.org/10.1002/aisy.202400739 (2025).
Caravelli, F., Milano, G., Ricciardi, C. & Kuncic, Z. Mean field theory of self-organizing nanowire connectomes. Ann. Phys. 535, 2300090 (2023).
Daniels, R. et al. Reservoir computing with 3D nanowire networks. Neural Netw. 154, 122–130 (2022).
Barrows, F. et al. A unifying approach to self-organizing systems interacting via conservation laws. Adv. Intell. Discov. https://doi.org/10.1002/aidi.202500190 (2026).
Miranda, E., Milano, G. & Ricciardi, C. Modeling of short-term synaptic plasticity effects in zno nanowire-based memristors using a potentiation-depression rate balance equation. IEEE Trans. Nanotechnol. 19, 609–612 (2020).
Merle, L., Delpoux, A., Mlayah, A. & Grisolia, J. Multiscale modeling of the dynamical conductivity of self-assembled nanoparticle networks: Numerical simulations vs analytical models. J. Appl. Phys. https://doi.org/10.1063/5.0097997 (2022).
Merle, L., Mlayah, A. & Grisolia, J. Plasmoelectronic properties of self-assembled gold nanoparticles: impedance spectroscopy experiments combined with numerical simulations. Mater. Today Nano 22, 100332 (2023).
Lazanas, A. C. & Prodromidis, M. I. Electrochemical impedance spectroscopy: a tutorial. ACS Meas. Sci. Au 3, 162–193 (2023).
Caravelli, F., Traversa, F. & Ventra, M. D. The complex dynamics of memristive circuits: analytical results and universal slow relaxation. Phys. Rev. E 95, (2017).
Barrows, F., Caravelli, F. & Sheldon, F. Network analysis of memristive device circuits: dynamics, stability andcorrelations. J. Phys. Complex. https://doi.org/10.1088/2632-072x/adb11e (2025).
Prokopenko, M. Guided self-organization. HFSP J. 3, 287–289 (2009).
Caravelli, F., Sheldon, F. C. & Traversa, F. L. Global minimization via classical tunneling assisted by collective force field formation. Sci. Adv. https://doi.org/10.1126/sciadv.abh1542 (2021).
Zapperi, S., Ray, P., Stanley, H. E. & Vespignani, A. Avalanches in breakdown and fracture processes. Phys. Rev. E 59, 5049–5057 (1999).
Wilson, K. G. & Fisher, M. E. Critical exponents in 3.99 dimensions. Phys. Rev. Lett. 28, 240–243 (1972).
Milano, G. et al. In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks. Nat. Mater. 21, 195–202 (2022).
Bartolucci, S., Caccioli, F., Caravelli, F. & Vivo, P. Emerging locality of network influence. Phys. Rev. Res. 5, 033123 (2023).
Bartolucci, S., Caccioli, F., Caravelli, F. & Vivo, P. Distribution of centrality measures on undirected random networks via the cavity method. Proc. Natl Acad. Sci. USA 121, e2403682121 (2024).
Caravelli, F. Cycle equivalence classes, orthogonal weingarten calculus, and the mean field theory of memristive systems. Neuromorphic Comput. Eng. 4, 024005 (2024).
Collins, B., Matsumoto, S. & Novak, J. The weingarten calculus. Not. Am. Math. Soc. 69, 1 (2022).
Tél, T. The joy of transient chaos. Chaos https://doi.org/10.1063/1.4917287 (2015).
Bak, P. How Nature Works: The Science of Self-Organized Criticality (Copernicus, 1996).
Yao, Z., Sun, K. & Wang, H. Dynamical mechanism of a memristor and its phase transition. Commun. Theor. Phys. 77, 055004 (2025).
Lukoševičius, M. & Jaeger, H. Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009).
Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019).
Nakajima, K. Physical reservoir computing — an introductory perspective. Jpn. J. Appl. Phys. 59, 060501 (2020).
Usami, Y. et al. In-materio reservoir computing in a sulfonated polyaniline network. Adv. Mater. 33, 2102688 (2021).
Liang, X. et al. Physical reservoir computing with emerging electronics. Nat. Electron. 7, 193–206 (2024).
Stepney, S. Physical reservoir computing: a tutorial. Nat. Comput. 23, 665 (2024).
Fu, K. et al. Reservoir computing with neuromemristive nanowire networks. In Proc. 2020 International Joint Conference on Neural Networks https://doi.org/10.1109/IJCNN48605.2020.9207727 (IEEE, 2020).
Mallinson, J. B. et al. Experimental demonstration of reservoir computing with self-assembled percolating networks of nanoparticles. Adv. Mater. 2402319 (2024).
Steel, J. K. et al. Speaker recognition and braille classification with a physical reservoir computing system. In Proc. 2025 International Joint Conference on Neural Networks https://doi.org/10.1109/IJCNN64981.2025.11229271 (IEEE, 2025).
Mallinson, J. B. et al. Reservoir computing using networks of memristors: effects of topology and heterogeneity. Nanoscale 15, 9663 (2023).
Demis, E. C. et al. Nanoarchitectonic atomic switch networks for unconventional computing. Jpn. J. Appl. Phys. 55, 1102B2 (2016).
Milano, G., Montano, K. & Ricciardi, C. In materia implementation strategies of physical reservoir computing with memristive nanonetworks. J. Phys. D 56, 084005 (2023).
Lin, J., Barrows, F. & Caravelli, F. Memristive linear algebra. Phys. Rev. Res. https://doi.org/10.1103/physrevresearch.7.023241 (2025).
Milano, G., Agliuzza, M., De Leo, N. & Ricciardi, C. Speech recognition through physical reservoir computing with neuromorphic nanowire networks. In Proc. 2022 International Joint Conference on Neural Networks 1–6 (IEEE, 2022).
Milano, G., Chakrabarty, T. & Ricciardi, C. Mackey-glass time series forecasting by nanowire networks. In Proc. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) 989–994 (IEEE, 2023).
Daniels, R. K. & Brown, S. A. Nanowire networks: how does small-world character evolve with dimensionality? Nanoscale Horiz. 6, 482 (2021).
Heywood, Z. et al. Self-organized nanoscale networks: are neuromorphic properties conserved in realistic device geometries? Neuromorphic Comput. Eng. 2, 024009 (2022).
Zhu, R. et al. Harnessing adaptive dynamics in neuro-memristive nanowire networks for transfer learning. In Proc. 2020 International Conference on Rebooting Computing https://doi.org/10.1109/ICRC2020.2020.00007 (IEEE, 2020).
Loeffler, A. et al. Modularity and multitasking in neuro-memristive reservoir networks. Neuromorphic Comput. Eng. 1, 014003 (2021).
Xu, Y., Gottwald, G. A. & Kuncic, Z. Learning chaotic dynamics with neuromorphic network dynamics. APL Mach. Learn. 3, 046104 (2025).
Xu, Y., Gottwald, G. A. & Kuncic, Z. Dynamic reservoir computing with physical neuromorphic networks. In Proc. 2025 International Joint Conference on Neural Networks 1–8 (IEEE, 2025).
Pistolesi, V., Ceni, A., Milano, G., Ricciardi, C. & Gallicchio, C. A memristive computational neural network model for time-series processing. APL Mach. Learn. 3, 016117 (2025).
Jaeger, H., Noheda, B. & van der Wiel, W. G. Toward a formal theory for computing machines made out of whatever physics offers. Nat. Commun. 14, 4911 (2023).
Diaz-Alvarez, A., Higuchi, R., Li, Q., Shingaya, Y. & Nakayama, T. Associative routing through neuromorphic nanowire networks. AIP Adv. 10, 025134 (2020).
Hu, S. et al. Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat. Commun. 6, 7522 (2015).
Pershin, Y. V. & Di Ventra, M. Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 23, 881–886 (2010).
Movellan, J. R. in Connectionist Models (eds Touretzky, D. S. et al.) https://doi.org/10.1016/B978-1-4832-1448-1.50007-X (Morgan Kaufmann, 1991).
Schuman, C. et al. Evolutionary vs imitation learning for neuromorphic control at the edge. Neuromorphic Comput. Eng. 2, 014002 (2022).
Mitchell, M. An Introduction to Genetic Algorithms (MIT Press, 1996).
Scellier, B. & Bengio, Y. Equilibrium propagation: bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci. https://doi.org/10.3389/fncom.2017.00024 (2017).
Stern, M., Hexner, D., Rocks, J. W. & Liu, A. J. Supervised learning in physical networks: From machine learning to learning machines. Phys. Rev. X 11, 021045 (2021).
Laborieux, A. et al. Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias. Front. Neurosci. https://doi.org/10.3389/fnins.2021.633674 (2021).
Stern, M., Dillavou, S., Miskin, M. Z., Durian, D. J. & Liu, A. J. Physical learning beyond the quasistatic limit. Phys. Rev. Res. https://doi.org/10.1103/physrevresearch.4.l022037 (2022).
Lin, J., Desai, A., Barrows, F. & Caravelli, F. How to train your resistive network: generalized equilibrium propagation and analytical learning. Preprint at https://arxiv.org/abs/2602.03546 (2026).
Smith, S., Purcell, C. & Kuncic, Z. Training-free ai for earth observation change detection using physics aware neuromorphic networks. Sci. Rep. 15, 35292 (2025).
Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).
Caravelli, F. & Carbajal, J. P. Memristors for the curious outsiders. Technologies 6, 118 (2018).
Wang, Z. et al. Resistive switching materials for information processing. Nat. Rev. Mater. 5, 173–195 (2020).
Waser, R. & Aono, M. Nanoionics-based resistive switching memories. Nat. Mater. 6, 833–840 (2007).
Valov, I., Waser, R., Jameson, J. R. & Kozicki, M. N. Electrochemical metallization memories — fundamentals, applications, prospects. Nanotechnology 22, 254003 (2011).
Wuttig, M. & Yamada, N. Phase-change materials for rewriteable data storage. Nat. Mater. 6, 824–832 (2007).
Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).
Milano, G. et al. Memristance and transmemristance in multiterminal memristive systems. Sci. Rep. https://doi.org/10.1038/s41598-026-35671-7 (2026).