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

Kun WangYitao LiaoWenhao LiJunlong LiHao SuRong ChenJae Hyeon ParkYongai ZhangXiongtu ZhouChaoxing WuZhiqiang LiuTailiang GuoTae Whan Kim
Published in: Nature communications (2024)
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.
Keyphrases
  • artificial intelligence
  • working memory
  • machine learning
  • big data
  • high resolution
  • deep learning
  • peripheral nerve
  • mass spectrometry
  • atomic force microscopy