Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Yue Yang, Fangduo Zhu, Xumeng Zhang (), Pei Chen, Yongzhou Wang, Jiaxue Zhu, Yanting Ding, Lingli Cheng, Chao Li, Hao Jiang, Zhongrui Wang, Peng Lin, Tuo Shi, Ming Wang, Qi Liu (), Ningsheng Xu and Ming Liu
Additional contact information
Yue Yang: Fudan University
Fangduo Zhu: Fudan University
Xumeng Zhang: Fudan University
Pei Chen: Fudan University
Yongzhou Wang: Institute of Microelectronics of Chinese Academy of Sciences
Jiaxue Zhu: Institute of Microelectronics of Chinese Academy of Sciences
Yanting Ding: Fudan University
Lingli Cheng: Fudan University
Chao Li: Fudan University
Hao Jiang: Fudan University
Zhongrui Wang: The University of Hong Kong
Peng Lin: Zhejiang University
Tuo Shi: Institute of Microelectronics of Chinese Academy of Sciences
Ming Wang: Fudan University
Qi Liu: Fudan University
Ningsheng Xu: Fudan University
Ming Liu: Fudan University

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

Abstract: Abstract Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Date: 2024
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DOI: 10.1038/s41467-024-48399-7

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