• search hit 6 of 10
Back to Result List

TiN/Ti/HfO2/TiN Memristive Devices for Neuromorphic Computing: From Synaptic Plasticity to Stochastic Resonance

  • 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.

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author: David Maldonado, Antonio Cantudo, Eduardo Perez, Rocio Romero-Zaliz, Emilio Perez-Bosch Quesada, Mamathamba Kalishettyhalli Mahadevaiah, Francisco Jimenez-Molinos, Christian WengerORCiD, Juan Bautista Roldan
DOI:https://doi.org/10.3389/fnins.2023.1271956
ISSN:1662-4548
Title of the source (English):Frontiers in Neuroscience
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2023
Tag:Neural network; RRAM
Volume/Year:17
Article number:1271956
Way of publication:Open Access
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.