TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance Maldonado Correa, David Cantudo Gómez, Antonio Romero Zaliz, Rocio Celeste Jiménez Molinos, Francisco Roldán Aranda, Juan Bautista Resistive switching devices Neuromorphic computing Synaptic behavior Spike-timing-dependent plasticity Stochastic resonance The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023. 1271956/full#supplementary-material Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The authors thank the support of the Consejeria de Conocimiento, Investigacion y Universidad, Junta de Andalucia (Spain), and the FEDER program through project B-TIC-624-UGR20. They also thank the support of the Federal Ministry of Education and Research of Germany under Grant 16ME0092. We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics. 2023-09-20T10:02:33Z 2023-09-20T10:02:33Z 2023 journal article Maldonado D, Cantudo A, Perez E, Romero-Zaliz R, Perez-Bosch Quesada E, Mahadevaiah MK, Jimenez-Molinos F, Wenger C and Roldan JB (2023) TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance. Front. Neurosci. 17:1271956. [doi: 10.3389/fnins.2023.1271956] https://hdl.handle.net/10481/84515 10.3389/fnins.2023.1271956 eng open access Frontiers