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• W4383212964 abstract "Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks." @default.
• W4383212964 created "2023-07-06" @default.
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• W4383212964 date "2023-07-07" @default.
• W4383212964 modified "2023-10-05" @default.
• W4383212964 title "Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks" @default.
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• W4383212964 doi "https://doi.org/10.1126/sciadv.adg5946" @default.
• W4383212964 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37406117" @default.
• W4383212964 hasPublicationYear "2023" @default.
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