journal article·2020-10

Dynamic Electrical Pathway Tuning in Neuromorphic Nanowire Networks

Qiao Li, Adrian Diaz‐Alvarez, Ryo Iguchi, Joel Hochstetter, Alon Loeffler, Ruomin Zhu, Yoshitaka Shingaya, Zdenka Kuncic, Ken‐ichi Uchida, Tomonobu Nakayama

Advanced Functional Materials· Volume 30· Issue 43
Publisher page

Abstract

Abstract Neurobiology‐inspired phenomena such as winner‐takes‐all competition and critical dynamics have been recently reported to arise in neuromorphic nanowire networks. These are unique systems where interactions between memristive elements creates emergent conductance pathways between discrete electrodes. This mode of operation can offer substantial advantages to create a truly concomitant plastic‐static system for integration in neuromorphic devices. However, critical aspects such as pathway controllability and stability are yet to be explored. In this study, pathway formation in self‐assembled neuromorphic networks formed by Ag nanowires decorated with TiO2 nanoparticles is investigated. Direct visualization of pathway formation through a neuromorphic network is attained using the lock‐in thermography technique. Using this technique, it is demonstrated that how networks preserve information from previously used pathways through increased local junction connectivity. This effect directly reshapes subsequent formation of pathways whenever the spatial location of the electrodes is dynamically changed. Combining these results with conventional current–voltage measurements, which show that the network electrically acts as a volatile switching memristor, a unique interaction between short‐term and long‐term memory arises. This produces unexpected collective dynamical states of potentiation and inhibition of network conductance whenever different spatiotemporal signals are dynamically fed to the network.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Neural dynamics and brain function

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki 305‐8571 Japan

International Center for Materials Nanoarchitectonics (WPI‐MANA) National Institute for Materials Science (NIMS) Tsukuba Ibaraki 305‐0044 Japan

Research Center for Magnetic and Spintronic Materials (CMSM) National Institute for Materials Science (NIMS) Tsukuba Ibaraki 305‐0047 Japan

Sydney Nano Institute and School of Physics University of Sydney Sydney NSW 2006 Australia

Institute for Materials Research Tohoku University Sendai 980‐8577 Japan

References (63)

Show source-provided references
1. 10.1063/1.503783510.1063/1.5037835 ↗
2. 10.1002/admt.20180058910.1002/admt.201800589 ↗
3. 10.1002/aelm.20180090910.1002/aelm.201800909 ↗
4. 10.1038/nmat305410.1038/nmat3054 ↗
5. 10.1002/adma.20090368010.1002/adma.200903680 ↗
6. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
7. 10.1166/nnl.2014.188810.1166/nnl.2014.1888 ↗
8. 10.1038/nature0693210.1038/nature06932 ↗
9. 10.1038/nmat202310.1038/nmat2023 ↗
10. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
11. 10.1002/adfm.20140130410.1002/adfm.201401304 ↗
12. 10.1103/PhysRevLett.102.02680110.1103/PhysRevLett.102.026801 ↗
13. 10.1002/adfm.20170486210.1002/adfm.201704862 ↗
14. 10.1038/s41467-018-04482-410.1038/s41467-018-04482-4 ↗
15. 10.1002/pssr.20190002910.1002/pssr.201900029 ↗
16. 10.1038/nmat475610.1038/nmat4756 ↗
17. 10.1002/adma.20160445710.1002/adma.201604457 ↗
18. 10.1038/s41467-017-00869-x10.1038/s41467-017-00869-x ↗
19. 10.1002/aisy.20190008410.1002/aisy.201900084 ↗
20. 10.1038/s41467-017-02337-y10.1038/s41467-017-02337-y ↗
21. 10.1038/s41598-017-05480-010.1038/s41598-017-05480-0 ↗
22. 10.1177/107385841666772010.1177/1073858416667720 ↗
23. 10.3389/fnins.2020.0018410.3389/fnins.2020.00184 ↗
24. 10.1371/journal.pone.004277210.1371/journal.pone.0042772 ↗
25. 10.1088/0957-4484/24/38/38400410.1088/0957-4484/24/38/384004 ↗
26. 10.1007/978-3-319-02630-5_1010.1007/978-3-319-02630-5_10 ↗
27. 10.7567/JJAP.53.01AA0210.7567/JJAP.53.01AA02 ↗
28. 10.1039/C8FD00109J10.1039/C8FD00109J ↗
29. 10.1109/TED.2017.276606310.1109/TED.2017.2766063 ↗
30. 10.1038/s41598-017-08244-y10.1038/s41598-017-08244-y ↗
31. 10.1103/PhysRevE.92.05213410.1103/PhysRevE.92.052134 ↗
32. 10.1088/1361-6528/ab76ec10.1088/1361-6528/ab76ec ↗
33. 10.1038/s41598-019-51330-610.1038/s41598-019-51330-6 ↗
34. Kuncic Z. · 2018 · 1
35. 10.1038/s41467-018-05517-610.1038/s41467-018-05517-6 ↗
36. 10.1038/nature0319010.1038/nature03190 ↗
37. 10.1088/1468-6996/16/4/04500410.1088/1468-6996/16/4/045004 ↗
38. 10.1002/adfm.20100152010.1002/adfm.201001520 ↗
39. 10.1039/C4NR02338B10.1039/C4NR02338B ↗
40. 10.1021/nl303416h10.1021/nl303416h ↗
41. Scharnhorst K. · 2017 · 133
42. 10.1103/PhysRevE.84.04670310.1103/PhysRevE.84.046703 ↗
43. 10.1016/j.matlet.2011.05.07610.1016/j.matlet.2011.05.076 ↗
44. 10.1016/j.jhazmat.2010.01.01310.1016/j.jhazmat.2010.01.013 ↗
45. 10.1021/nl904092h10.1021/nl904092h ↗
46. 10.1063/1.503781710.1063/1.5037817 ↗
47. 10.1007/978-3-642-02417-710.1007/978-3-642-02417-7 ↗
48. 10.1080/17686733.2018.156334910.1080/17686733.2018.1563349 ↗
49. 10.1038/s41586-018-0143-x10.1038/s41586-018-0143-x ↗
50. 10.1038/ncomms1375410.1038/ncomms13754 ↗
51. 10.1103/PhysRevB.98.01440210.1103/PhysRevB.98.014402 ↗
52. 10.1038/srep2823310.1038/srep28233 ↗
53. 10.1038/s41598-019-50802-z10.1038/s41598-019-50802-z ↗
54. 10.1021/acs.nanolett.6b0327010.1021/acs.nanolett.6b03270 ↗
55. 10.1126/sciadv.aau340710.1126/sciadv.aau3407 ↗
56. Q.Li A.Diaz‐Alvarez D.Tang R.Higuchi Y.Shingaya T.Nakayama Sleep‐Dependent Memory Consolidation in a Neuromorphic Nanowire Network 2020.10.1021/acsami.0c11157 ↗
57. 10.1063/1.514057910.1063/1.5140579 ↗
58. 10.1021/acsami.7b0483910.1021/acsami.7b04839 ↗
59. 10.1021/nn101066710.1021/nn1010667 ↗
60. 10.1021/acsami.7b1066610.1021/acsami.7b10666 ↗
61. 10.1007/s11432-017-9424-y10.1007/s11432-017-9424-y ↗
62. 10.1021/nl034312m10.1021/nl034312m ↗
63. 10.1126/science.107722910.1126/science.1077229 ↗