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Updated: Jan 4, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Robust Memristor Networks for Neuromorphic Computation Applications
Dániel Hajtó1, Ádám Rák2, György Cserey3,4
1Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary. hajto.daniel@itk.ppke.hu.
Abstract:
One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.
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