Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves

Kun Wang, Yitao Liao, Wenhao Li, Junlong Li, Hao Su, Rong Chen, Jae Hyeon Park, Yongai Zhang, Xiongtu Zhou, Chaoxing Wu (), Zhiqiang Liu (), Tailiang Guo () and Tae Whan Kim ()
Additional contact information
Kun Wang: Fuzhou University
Yitao Liao: Fuzhou University
Wenhao Li: Fuzhou University
Junlong Li: Fuzhou University
Hao Su: Fuzhou University
Rong Chen: Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China
Jae Hyeon Park: Hanyang University
Yongai Zhang: Fuzhou University
Xiongtu Zhou: Fuzhou University
Chaoxing Wu: Fuzhou University
Zhiqiang Liu: Chinese Academy of Sciences
Tailiang Guo: Fuzhou University
Tae Whan Kim: Hanyang University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1038/s41467-024-47641-6

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