AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Imbuing artificial sensory system with intelligence of the biological counterpart is limited by challenges in emulating perceptual learning ability at the device level. In biological systems, stimuli from the surrounding environment are detected, transmitted, and processed by receptor, afferent nerve, and brain, respectively. This process allows the living creatures to identify the potential hazards and improve their adaptability in various environments. Here, inspired by the biological olfaction system, a gas sensory system with perceptual learning is developed. As a proof-of-concept, H2S gas with various concentrations is used as the stimulation and the stimuli will be converted to pulse-like physiological signals in the designed system, which consists of a gas sensor, a flexible oscillator, and a memristor-type artificial synapse. Furthermore, the learning ability is implemented using a supervised learning method based on k-nearest neighbors (KNN) algorithm. The recognition accuracy can be enhanced by repeating training, illustrating a great potential to be used as the neuromorphic sensory system with a learning ability for the applications in robotics.
Luo, Z. Q.; Weiss, D. E.; Liu, Q. Y.; Tian, B. Z. Biomimetic approaches toward smart bio-hybrid systems. Nano Res. 2018, 11, 3009–3030.
Jung, Y. H.; Park, B.; Kim, J. U.; Kim, T. I. Bioinspired electronics for artificial sensory systems. Adv. Mater. 2019, 31, 1803637.
Chalasani, S. H.; Chronis N.; Tsunozaki M.; Gray J. M.; Ramot D.; Goodman M. B.; Bargmann C. I. Dissecting a circuit for olfactory behaviour in Caenorhabditis elegans. Nature 2007, 450, 63–70.
Sarafoleanu, C.; Mella, C.; Georgescu, M.; Perederco, C. The importance of the olfactory sense in the human behavior and evolution. J. Med. Life 2009, 2, 196–198.
Neumann, P. P.; Kohlhoff, H.; Hüllmann, D.; Lilienthal, A. J.; Kluge, M. Bringing Mobile Robot Olfaction to the next dimension—UAV- based remote sensing of gas clouds and source localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp 3910–3916.
Wu, Y. Z.; Liu, Y. W.; Zhou, Y. L.; Man, Q. K.; Hu, C.; Asghar, W.; Li, F. L.; Yu, Z.; Shang, J.; Liu, G. et al. A skin-inspired tactile sensor for smart prosthetics. Sci. Robot. 2018, 3, eaat0429.
Asada, M.; Hosoda, K.; Kuniyoshi, Y.; Ishiguro, H.; Inui, T.; Yoshikawa, Y.; Ogino, M.; Yoshida, C. Cognitive developmental robotics: A survey. IEEE Trans. Auton. Ment. Dev. 2009, 1, 12–34.
Richardson, J. T. E.; Zucco, G. M. Cognition and olfaction: A review. Psychol. Bull. 1989, 105, 352–360.
Morgavi, G.; Marconi, L.; Morando, M.; Cutugno, P. From human creative cognitive processes to adaptable artificial system design. In Attention, Representation, and Human Performance: Integration of Cognition, Emotion, and Motivation. Masmoudi, S.; Dai, D. Y.; Naceur, A., Eds.; Psychology Press: New York, 2012.
Kim, Y.; Chortos, A.; Xu, W. T.; Liu, Y. X.; Oh, J. Y.; Son, D.; Kang, J.; Foudeh, A. M.; Zhu, C. X.; Lee, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 2018, 360, 998–1003.
Wan, C. J.; Chen, G.; Fu, Y. M.; Wang, M.; Matsuhisa, N.; Pan, S. W.; Pan, L.; Yang, H.; Wan, Q.; Zhu, L. Q. et al. An artificial sensory neuron with tactile perceptual learning. Adv. Mater. 2018, 30, 1801291.
He, K.; Liu, Y. Q.; Wang, M.; Chen, G.; Jiang, Y.; Yu, J. C.; Wan, C. J.; Qi, D. P.; Xiao, M.; Leow, W. R. et al. An artificial somatic reflex arc. Adv. Mater. 2020, 32, 1905399.
Galstyan, V.; Poli, N.; Comini, E. Highly sensitive and selective H2S chemical sensor based on ZnO nanomaterial. Appl. Sci. 2019, 9, 1167.
Mirzaei, A.; Kim, S. S.; Kim, H. W. Resistance-based H2S gas sensors using metal oxide nanostructures: A review of recent advances. J. Hazard. Mater. 2018, 357, 314–331.
Galstyan, V.; Bhandari, M. P.; Sberveglieri, V.; Sberveglieri, G.; Comini, E. Metal oxide nanostructures in food applications: Quality control and packaging. Chemosensors 2018, 6, 16.
Roach, K. A.; Tobler, M.; Winemiller, K. O. Hydrogen sulfide, bacteria, and fish: A unique, subterranean food chain. Ecology 2011, 92, 2056–2062.
Li, G. H.; Wang, X. W.; Liu, L.; Liu, R.; Shen, F. P.; Cui, Z.; Chen, W.; Zhang, T. Controllable synthesis of 3D Ni(OH)2 and NiO nanowalls on various substrates for high-performance nanosensors. Small 2015, 11, 731–739.
Lu, Q. F.; Sun, F. Q.; Liu, L.; Li, L. H.; Wang, Y. Y.; Hao, M. M.; Wang, Z. H.; Wang, S. Q.; Zhang, T. Biological receptor-inspired flexible artificial synapse based on ionic dynamics. Microsyst. Nanoeng. 2020, 6, 84.
Lesniak, D. R.; Marshall, K. L.; Wellnitz, S. A.; Jenkins, B. A.; Baba, Y.; Rasband, M. N.; Gerling, G. J.; Lumpkin, E. A. Computation identifies structural features that govern neuronal firing properties in slowly adapting touch receptors. Elife 2014, 3, e01488.
Zhu, L. Q.; Wan, C. J.; Guo, L. Q.; Shi, Y.; Wan, Q. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat. Commun. 2014, 5, 3158.
Westerman, W. C.; Northmore, D. P. M.; Elias, J. G. Neuromorphic synapses for artificial dendrites. In Neuromorphic Systems Engineering: Neural Networks in Silicon. Lande, T. S., Ed.; Springer: Boston, 1998; pp 339–365.
Langley, P. The changing science of machine learning. Mach. Learn. 2011, 82, 275–279.
Mohri, M.; Rostamizadeh, A.; Talwalkar, A. Foundations of Machine Learning; The MIT Press: Massachusetts, 2012.
