The DOI matches its Crossref record; 264 of the 264 references it cites were found in a registry or an index.
Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges
Abstract
As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.
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1 retracted
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Cited by
Two-dimensional materials for next-generation computing technologies
Liu, C., Chen, H., Wang, S., Liu, Q., Jiang, Y.-G., Zhang, D. W., Liu, M., & Zhou, P. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0724-3 · Cited by 1,045
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
(2021) · doi:10.1038/s41467-020-20692-1 · Cited by 597
Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays
Li, Y., & Ang, K.-W. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202000137 · Cited by 220
Self-citationNanostructured perovskites for nonvolatile memory devices
Liu, Q., Gao, S., Xu, L., Yue, W., Zhang, C., Kan, H., Li, Y., & Shen, G. (2022) · Chemical Society Reviews · doi:10.1039/d1cs00886b · Cited by 163
Self-citationA Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing
Tang, J., He, C., Tang, J., Yue, K., Zhang, Q., Liu, Y., Wang, Q., Wang, S., Li, N., Shen, C., et al. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202011083 · Cited by 163
Self-citation
Metadata
- Authors
- Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., Gao, B., Qian, H., Bi, G., Song, S., Yang, J. J., & Wu, H.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2019-09-24
- Volume
- 31
- Issue
- 49
- Pages
- e1902761-e1902761
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2976743137
- Cited by
- 834
- References
- 264
- FWCI
- 24.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., Gao, B., Qian, H., Bi, G., Song, S., Yang, J. J., & Wu, H. (2019). Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges. Advanced Materials. https://doi.org/10.1002/adma.201902761
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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