Artificial synapse network on inorganic proton conductor for neuromorphic systems

750 indexed citations

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This paper, published in 2014, received 750 indexed citations. Written by Li Qiang Zhu, Chang Wan, Li Guo, Yi Shi and Qing Wan covering the research area of Cellular and Molecular Neuroscience, Electrical and Electronic Engineering and Polymers and Plastics. It is primarily cited by scholars working on Electrical and Electronic Engineering (715 citations), Cellular and Molecular Neuroscience (437 citations) and Polymers and Plastics (205 citations). Published in Nature Communications.

Countries where authors are citing Artificial synapse network on inorganic proton conductor for neuromorphic systems

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This map shows the geographic impact of Artificial synapse network on inorganic proton conductor for neuromorphic systems. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Artificial synapse network on inorganic proton conductor for neuromorphic systems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Artificial synapse network on inorganic proton conductor for neuromorphic systems more than expected).

Fields of papers citing Artificial synapse network on inorganic proton conductor for neuromorphic systems

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Artificial synapse network on inorganic proton conductor for neuromorphic systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1038/ncomms4158.

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