Page 145 - Read Online

Page 22 of 26 Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77

Revised the manuscript, provided supervision and acquired funding: Ma, B.; Yuan, W.; Ye, T.
All authors have read the manuscript and approved the final version.

Availability of data and materials
Not applicable.

Financial support and sponsorship
This work was supported by the Fundamental Research Funds for the Central Universities (D5000220072).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2025.

REFERENCES
1. Yu, Z.; Zahid, A.; Taha, A.; et al. An intelligent implementation of multi-sensing data fusion with neuromorphic computing for
human activity recognition. IEEE. Internet. Things. J. 2023, 10, 1124-33. DOI
2. Ji, X.; Zhao, X.; Tan, M. C.; Zhao, R. Artificial perception built on memristive system: visual, auditory, and tactile sensations. Adv.
Intell. Syst. 2020, 2, 1900118. DOI
3. Lee, J. P.; Jang, H.; Jang, Y.; et al. Encoding of multi-modal emotional information via personalized skin-integrated wireless facial
interface. Nat. Commun. 2024, 15, 530. DOI PubMed PMC
4. Xiong, W.; Zhu, C.; Guo, D.; et al. Bio-inspired, intelligent flexible sensing skin for multifunctional flying perception. Nano. Energy.
2021, 90, 106550. DOI
5. Zhao, C.; Park, J.; Root, S. E.; Bao, Z. Skin-inspired soft bioelectronic materials, devices and systems. Nat. Rev. Bioeng. 2024, 2,
671-90. DOI
6. Wan, T.; Shao, B.; Ma, S.; Zhou, Y.; Li, Q.; Chai, Y. In-sensor computing: materials, devices, and integration technologies. Adv.
Mater. 2023, 35, 2203830. DOI
7. Wang, Z.; Wang, Y.; Zhang, X.; et al. Flexible photovoltaic micro-power system enabled with a customized MPPT. Appl. Energy.
2024, 367, 123425. DOI
8. Milakov, M. Deep learning with GPUs. https://www.nvidia.co.uk/docs/IO/147844/Deep-Learning-With-GPUs-MaximMilakov-
NVIDIA.pdf. (accessed 2025-03-14).
9. Cox, D. D.; Dean, T. Neural networks and neuroscience-inspired computer vision. Curr. Biol. 2014, 24, R921-9. DOI PubMed
10. Yoon, S. J.; Park, J. T.; Lee, Y. K. The neuromorphic computing for biointegrated electronics. Soft. Sci. 2024, 4, 30. DOI
11. Han, J.; Yun, S.; Lee, S.; Yu, J.; Choi, Y. A review of artificial spiking neuron devices for neural processing and sensing. Adv. Funct.
Mater. 2022, 32, 2204102. DOI
12. Torricelli, F.; Adrahtas, D. Z.; Bao, Z.; et al. Electrolyte-gated transistors for enhanced performance bioelectronics. Nat. Rev.
Methods. Primers. 2021, 1, 66. DOI PubMed PMC
13. Jang, Y.; Park, J.; Kang, J.; Lee, S. Amorphous InGaZnO (a-IGZO) synaptic transistor for neuromorphic computing. ACS. Appl.
Electron. Mater. 2022, 4, 1427-48. DOI
14. Song, M. K.; Kang, J. H.; Zhang, X.; et al. Recent advances and future prospects for memristive materials, devices, and systems.
ACS. Nano. 2023, 17, 11994-2039. DOI
15. Tong, P.; Wang, W.; Xu, H.; et al. Highly parallel writing strategy based on diagonal-gates-connection 1T1R arrays. IEEE. Trans.
Electron. Devices. 2022, 69, 6693-8. DOI
16. Song, H.; Lee, M. G.; Kim, G.; et al. Fully memristive elementary motion detectors for a maneuver prediction. Adv. Mater. 2024, 36,
e2309708. DOI
17. Wu, Z.; Lu, J.; Shi, T.; et al. A habituation sensory nervous system with memristors. Adv. Mater. 2020, 32, e2004398. DOI
18. Li, Q. X.; Liu, Y. L.; Cao, Y. Y.; et al. Ferroelectric artificial synapse for neuromorphic computing and flexible applications.