journal article·2024-09

Bioinspired Artificial Visual‐Respiratory Synapse as Multimodal Scene Recognition System with Oxidized‐Vacancies MXene

Dongchen Tan, Zhaorui Zhang, Haohao Shi, Nan Sun, Qikun Li, Sheng Bi, Jijie Huang, Yiheng Liu, Qinglei Guo, Chengming Jiang

Advanced Materials· Volume 36· Issue 36
Publisher page

Abstract

Abstract In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual‐respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and “learning‐experience” behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl‐preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human‐like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human‐like mixed‐modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Photoreceptor and optogenetics research · Neural dynamics and brain function

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

Affiliations

State Key Laboratory of High‐Performance Precision Manufacturing Dalian University of Technology Dalian 116024 China

School of Advanced Materials and Nanotechnology Xidian University Xi'an 710126 China

School of Materials Engineering Purdue University West Lafayette IN 47907 USA

Department of Material Science and Engineering Frederick Seitz Material Research Laboratory University of Illinois at Urbana‐Champaign Urbana IL 61801 USA

Funding

National Natural Science Foundation of China

References (70)

Show source-provided references
1. 10.1016/j.neuron.2015.09.02910.1016/j.neuron.2015.09.029 ↗
2. 10.1126/science.aay806410.1126/science.aay8064 ↗
3. 10.1016/j.neuron.2019.08.03810.1016/j.neuron.2019.08.038 ↗
4. 10.1523/JNEUROSCI.4678-12.201310.1523/JNEUROSCI.4678-12.2013 ↗
5. Rowland B. A. · Neuroscience · 2014 · 41 · 78
6. 10.1152/jn.00286.201210.1152/jn.00286.2012 ↗
7. 10.1038/s41593-018-0076-610.1038/s41593-018-0076-6 ↗
8. 10.1016/j.brs.2012.04.01110.1016/j.brs.2012.04.011 ↗
9. 10.1126/sciadv.abd911710.1126/sciadv.abd9117 ↗
10. 10.1126/sciadv.add695010.1126/sciadv.add6950 ↗
11. 10.1126/sciadv.150132610.1126/sciadv.1501326 ↗
12. 10.1038/s41928-022-00836-510.1038/s41928-022-00836-5 ↗
13. 10.1038/s41928-022-00847-210.1038/s41928-022-00847-2 ↗
14. 10.1038/s41467-017-02615-910.1038/s41467-017-02615-9 ↗
15. 10.1038/s41467-023-36480-610.1038/s41467-023-36480-6 ↗
16. 10.1002/inf2.1247810.1002/inf2.12478 ↗
17. 10.1002/adma.20190689910.1002/adma.201906899 ↗
18. 10.1038/s41566-020-00754-y10.1038/s41566-020-00754-y ↗
19. 10.1038/s42254-020-0208-210.1038/s42254-020-0208-2 ↗
20. 10.1016/j.nanoen.2021.10611610.1016/j.nanoen.2021.106116 ↗
21. 10.1038/s41928-018-0103-310.1038/s41928-018-0103-3 ↗
22. 10.1126/science.adc915010.1126/science.adc9150 ↗
23. 10.1038/s41928-017-0006-810.1038/s41928-017-0006-8 ↗
24. 10.1021/acs.accounts.8b0055310.1021/acs.accounts.8b00553 ↗
25. 10.1038/s41467-023-36935-w10.1038/s41467-023-36935-w ↗
26. 10.1088/2631-7990/ad339b10.1088/2631-7990/ad339b ↗
27. 10.1038/s41467-019-13599-z10.1038/s41467-019-13599-z ↗
28. 10.1126/science.aai798410.1126/science.aai7984 ↗
29. 10.1016/j.mattod.2022.09.01210.1016/j.mattod.2022.09.012 ↗
30. 10.1021/acsnano.0c0892110.1021/acsnano.0c08921 ↗
31. 10.1002/adfm.20210014410.1002/adfm.202100144 ↗
32. 10.1002/adma.20230560910.1002/adma.202305609 ↗
33. 10.1002/adfm.20231301010.1002/adfm.202313010 ↗
34. 10.1002/adfm.20220095910.1002/adfm.202200959 ↗
35. 10.1038/s41928-020-0422-z10.1038/s41928-020-0422-z ↗
36. 10.1146/annurev-neuro-090121-01442410.1146/annurev-neuro-090121-014424 ↗
37. 10.1038/s41593-023-01425-110.1038/s41593-023-01425-1 ↗
38. 10.1021/acsnano.6b0524010.1021/acsnano.6b05240 ↗
39. 10.1002/adma.20210339310.1002/adma.202103393 ↗
40. 10.1021/acs.chemmater.7b0284710.1021/acs.chemmater.7b02847 ↗
41. 10.1002/advs.20220274810.1002/advs.202202748 ↗
42. 10.1002/inf2.1232810.1002/inf2.12328 ↗
43. 10.1016/j.pmatsci.2020.10075710.1016/j.pmatsci.2020.100757 ↗
44. 10.3390/ma1501030710.3390/ma15010307 ↗
45. 10.1021/acs.chemmater.0c0035910.1021/acs.chemmater.0c00359 ↗
46. 10.1021/acsnano.1c0024810.1021/acsnano.1c00248 ↗
47. 10.1021/acsnano.8b0237910.1021/acsnano.8b02379 ↗
48. 10.1021/acsnano.9b0034010.1021/acsnano.9b00340 ↗
49. 10.1002/adfm.20221161010.1002/adfm.202211610 ↗
50. 10.1103/PhysRevB.33.402710.1103/PhysRevB.33.4027 ↗
51. 10.1103/PhysRevB.59.981010.1103/PhysRevB.59.9810 ↗
52. 10.1002/adma.20180622710.1002/adma.201806227 ↗
53. 10.1002/adma.20170540010.1002/adma.201705400 ↗
54. 10.1016/j.cej.2024.14962010.1016/j.cej.2024.149620 ↗
55. 10.1002/aenm.20210386710.1002/aenm.202103867 ↗
56. 10.1002/adma.20200209210.1002/adma.202002092 ↗
57. 10.1002/adma.20160608810.1002/adma.201606088 ↗
58. 10.1038/s41928-018-0118-910.1038/s41928-018-0118-9 ↗
59. 10.1002/adfm.20231288510.1002/adfm.202312885 ↗
60. 10.1021/acsnano.9b0768710.1021/acsnano.9b07687 ↗
61. 10.1038/nmat475610.1038/nmat4756 ↗
62. 10.1126/sciadv.adg594610.1126/sciadv.adg5946 ↗
63. 10.1056/NEJMoa170113110.1056/NEJMoa1701131 ↗
64. 10.1371/journal.pone.012064410.1371/journal.pone.0120644 ↗
65. 10.1039/D3TC00687E10.1039/D3TC00687E ↗
66. 10.1002/adma.20230738010.1002/adma.202307380 ↗
67. 10.1021/acsami.3c0642910.1021/acsami.3c06429 ↗
68. 10.1007/s40820-024-01330-710.1007/s40820-024-01330-7 ↗
69. 10.1002/adma.20220079310.1002/adma.202200793 ↗
70. 10.1002/adfm.20230905810.1002/adfm.202309058 ↗