Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nervesopen access
- Authors
- Wang, Kun; Liao, Yitao; Li, Wenhao; Li, Junlong; Su, Hao; Chen, Rong; Park, Jae Hyeon; Zhang, Yongai; Zhou, Xiongtu; Wu, Chaoxing; Liu, Zhiqiang; Guo, Tailiang; Kim, Tae Whan
- Issue Date
- Apr-2024
- Publisher
- Nature Publishing Group
- Citation
- Nature Communications, v.15, no.1, pp 1 - 11
- Pages
- 11
- Indexed
- SCIE
SCOPUS
- Journal Title
- Nature Communications
- Volume
- 15
- Number
- 1
- Start Page
- 1
- End Page
- 11
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/209691
- DOI
- 10.1038/s41467-024-47641-6
- ISSN
- 2041-1723
2041-1723
- Abstract
- The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.
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