Perovskite neural trees.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
07 05 2020
07 05 2020
Historique:
received:
26
03
2020
accepted:
07
04
2020
entrez:
9
5
2020
pubmed:
10
5
2020
medline:
10
5
2020
Statut:
epublish
Résumé
Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.
Identifiants
pubmed: 32382036
doi: 10.1038/s41467-020-16105-y
pii: 10.1038/s41467-020-16105-y
pmc: PMC7206050
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
2245Références
Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X 2, 89–124 (2017).
Wang, Y. et al. Mott-transition-based RRAM. Mater. Today 28, 63–80 (2019).
doi: 10.1016/j.mattod.2019.06.006
Guo, Y., Wu, H., Gao, B. & Qian, H. Unsupervised learning on resistive memory array based spiking neural networks. Front. Neurosci. 13, 812 (2019).
pubmed: 31447634
pmcid: 6691091
doi: 10.3389/fnins.2019.00812
Zhou, Y. & Ramanathan, S. Mott memory and neuromorphic devices. Proc. IEEE 103, 1289–1310 (2015).
doi: 10.1109/JPROC.2015.2431914
Andrews, J. L., Santos, D. A., Meyyappan, M., Williams, R. S. & Banerjee, S. Building brain-inspired logic circuits from dynamically switchable transition-metal oxides. Trends Chem. 1, 711–726 (2019).
doi: 10.1016/j.trechm.2019.07.005
Strukov, D. B. & Kohlstedt, H. Resistive switching phenomena in thin films: materials, devices, and applications. MRS Bull. 37, 108–114 (2012).
doi: 10.1557/mrs.2012.2
Keim, N. C., Paulsen, J. D., Zeravcic, Z., Sastry, S. & Nagel, S. R. Memory formation in matter. Rev. Mod. Phys. 91, 35002 (2019).
doi: 10.1103/RevModPhys.91.035002
Fuller, E. J. et al. Redox transistors for neuromorphic computing. IBM J. Res. Dev. 63, 1–9 (2019).
doi: 10.1147/JRD.2019.2942285
Kuzum, D., Yu, S. & Wong, H.-S. P. Synaptic electronics: materials, devices and applications. Nanotechnology 24, 382001 (2013).
pubmed: 23999572
doi: 10.1088/0957-4484/24/38/382001
Serb, A. et al. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat. Commun. 7, 12611 (2016).
pubmed: 27681181
pmcid: 5056401
doi: 10.1038/ncomms12611
Saïghi, S. et al. Plasticity in memristive devices for spiking neural networks. Front. Neurosci. 9, 51 (2015).
pubmed: 25784849
pmcid: 4345885
doi: 10.3389/fnins.2015.00051
Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci. 5, 97–107 (2004).
pubmed: 14735113
doi: 10.1038/nrn1327
Zhuang, X. et al. Hyperactivity and impaired response habituation in hyperdopaminergic mice. Proc. Natl Acad. Sci. USA 98, 1982–1987 (2001).
pubmed: 11172062
doi: 10.1073/pnas.98.4.1982
Irie, T. & Ohmori, H. Presynaptic GABAB receptors modulate synaptic facilitation and depression at distinct synapses in fusiform cells of mouse dorsal cochlear nucleus. Biochem. Biophys. Res. Commun. 367, 503–508 (2008).
pubmed: 18190780
doi: 10.1016/j.bbrc.2008.01.001
Yamamoto, K., Noguchi, J., Yamada, C., Watabe, A. M. & Kato, F. Distinct target cell-dependent forms of short-term plasticity of the central visceral afferent synapses of the rat. BMC Neurosci. 11, 134 (2010).
Durand, D. & Carlen, P. L. Impairment of long-term potentiation in rat hippocampus following chronic ethanol treatment. Brain Res. 308, 325–332 (1984).
pubmed: 6541071
doi: 10.1016/0006-8993(84)91072-2
Feigel’man, M. V. & Ioffe, L. B. in Models of Neural Networks I. Physics of Neural Networks (eds E., D., J.L., van H. & K., S.) 181–200 (Springer, 1991).
Sakaguchi, H. A hierarchical neural network model for category detection. Prog. Theor. Phys. 81, 321–328 (1989).
doi: 10.1143/PTP.81.321
Dotsenko, V. An Introduction to the Theory of Spin Glasses and Neural Networks. World Scientific Lecture Notes in Physics Vol. 54 (WORLD SCIENTIFIC, 1995).
Willcox, C. R. Exponential storage and retrieval in hierarchical neural networks. J. Phys. A. Math. Gen. 22, 4707–4728 (1989).
doi: 10.1088/0305-4470/22/21/032
Cortes, C., Krogh, A. & Hertz, J. A. Hierarchical associative networks. J. Phys. A Gen. Phys. 20, 4449–4455 (1987).
doi: 10.1088/0305-4470/20/13/044
Van Hemmen, J. L. Spin-glass models of a neural network. Phys. Rev. A 32, 1007–1018 (1985).
doi: 10.1103/PhysRevA.32.1007
Amit, D. J., Gutfreund, H. & Sompolinsky, H. Storing infinite numbers of patterns in a spin-glass model of neural networks. Phys. Rev. Lett. 55, 1530–1533 (1985).
pubmed: 10031847
doi: 10.1103/PhysRevLett.55.1530
Suzuki, I. S. & Suzuki, M. Effect of random disorder and spin frustration on the reentrant spin-glass and ferromagnetic phases in the stage-2 Cu
doi: 10.1103/PhysRevB.73.094448
Nagata, S., Keesom, P. H. & Harrison, H. R. Low-dc-field susceptibility of CuMn spin glass. Phys. Rev. B 19, 1633–1638 (1979).
doi: 10.1103/PhysRevB.19.1633
Hartnett, G. S., Parker, E. & Geist, E. Replica symmetry breaking in bipartite spin glasses and neural networks. Phys. Rev. E 98, 022116 (2018).
pubmed: 30253474
doi: 10.1103/PhysRevE.98.022116
Hartnett, G. S. & Mohseni, M. Self-supervised learning of generative spin-glasses with normalizing flows. Preprint at http://arxiv.org/abs/2001.00585 (2020).
Catalan, G. Progress in perovskite nickelate research. Phase Transit. 81, 729–749 (2008).
doi: 10.1080/01411590801992463
Catalano, S. et al. Rare-earth nickelates RNiO
doi: 10.1088/1361-6633/aaa37a
Keimer, B., Maier, J. & Mannhart, J. Electronic materials through time. Nat. Mater. 11, 751–752 (2012).
pubmed: 22918316
doi: 10.1038/nmat3407
Oh, C., Jo, M. & Son, J. All-solid-state synaptic transistors with high-temperature stability using proton pump gating of strongly correlated materials. ACS Appl. Mater. Interfaces 11, 15733–15740 (2019).
pubmed: 30968690
doi: 10.1021/acsami.9b00392
Ramadoss, K. et al. Proton-doped strongly correlated perovskite nickelate memory devices. IEEE Electron Device Lett. 39, 1500–1503 (2018).
Van De Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).
pubmed: 28218920
doi: 10.1038/nmat4856
Kawamoto, D. et al. Correlation between Ni valence and resistance modulation on a SmNiO
doi: 10.1021/acsaelm.8b00028
Massa, N. E. et al. Temperature and high-pressure dependent x-ray absorption of SmNiO
doi: 10.1088/2053-1591/2/12/126301
Zhou, Y. et al. Strongly correlated perovskite fuel cells. Nature 534, 231–234 (2016).
pubmed: 27279218
doi: 10.1038/nature17653
Shi, J., Zhou, Y. & Ramanathan, S. Colossal resistance switching and band gap modulation in a perovskite nickelate by electron doping. Nat. Commun. 5, 4860 (2014).
pubmed: 25181992
doi: 10.1038/ncomms5860
Mansour, A. N. & Melendres, C. A. X-ray absorption spectra and the local structure of nickel in some oxycompounds and fluorides. J. Phys. IV Fr. 7, 1171 (1997).
doi: 10.1051/jp4:19972178
Zhao, L. et al. Multi-level control of conductive nano-filament evolution in HfO2 ReRAM by pulse-train operations. Nanoscale 6, 5698–5702 (2014).
pubmed: 24769626
doi: 10.1039/C4NR00500G
Stathopoulos, S. et al. Multibit memory operation of metal-oxide Bi-layer memristors. Sci. Rep. 7, 1–7 (2017).
doi: 10.1038/s41598-017-17785-1
Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015).
doi: 10.1002/adfm.201501427
Goodman, D. & Brette, R. The brian simulator. Front. Neurosci. 3, 26 (2009).
doi: 10.3389/neuro.01.026.2009
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).
doi: 10.1109/5.726791
Diehl, P. & Cook, M. Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015).
pubmed: 26941637
pmcid: 4522567
doi: 10.3389/fncom.2015.00099
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169 (1996).
doi: 10.1103/PhysRevB.54.11169
Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).
doi: 10.1103/PhysRevB.50.17953
Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758 (1999).
doi: 10.1103/PhysRevB.59.1758
Liechtenstein, A. I., Anisimov, V. I. & Zaanen, J. Density-functional theory and strong interactions: Orbital ordering in Mott-Hubbard insulators. Phys. Rev. B 52, R5467 (1995).
doi: 10.1103/PhysRevB.52.R5467
Persson, K. Materials Data on SmNiO
Zhang, Z. et al. Perovskite nickelates as electric-field sensors in salt water. Nature 553, 68 (2017).
pubmed: 29258293
doi: 10.1038/nature25008
Zhang, H.-T. et al. Perovskite nickelates as bio-electronic interfaces. Nat. Commun. 10, 1651 (2019).
pubmed: 30971693
pmcid: 6458181
doi: 10.1038/s41467-019-09660-6
Kim, S. et al. Physical electro-thermal model of resistive switching in bi-layered resistance-change memory. Sci. Rep. 3, 1680 (2013).
pubmed: 23604263
pmcid: 3631947
doi: 10.1038/srep01680
Mott, M. F. & Gurney, R. W. Electronic Processes in Ionic Crystals (Oxford Clarendon Press, 1948).
Hooda, M. K. & Yadav, C. S. Electronic properties and the nature of metal–insulator transition in NdNiO3 prepared at ambient oxygen pressure. Phys. B Condens. Matter. 491, 31–36 (2016).
doi: 10.1016/j.physb.2016.03.014
Yan, H. et al. Multimodal hard x-ray imaging with resolution approaching 10 nm for studies in material sciences. Nano Futures 2, 011001 (2018).
Nazaretski, E. et al. Design and performance of an X-ray scanning microscope at the Hard X-ray Nanoprobe beamline of NSLS-II. J. Synchrotron Radiat. 24, 1113–1119 (2017).
pubmed: 29091054
doi: 10.1107/S1600577517011183
Winarski, R. P. et al. A hard X-ray nanoprobe beamline for nanoscale microscopy. J. Synchrotron Radiat. 19, 1056–1060 (2012).
pubmed: 23093770
pmcid: 3579591
doi: 10.1107/S0909049512036783