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TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance.

ABSTRACT: 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.

SUBMITTER: Maldonado D 

PROVIDER: S-EPMC10546015 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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TiN/Ti/HfO<sub>2</sub>/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance.

Maldonado David D   Cantudo Antonio A   Perez Eduardo E   Romero-Zaliz Rocio R   Perez-Bosch Quesada Emilio E   Mahadevaiah Mamathamba Kalishettyhalli MK   Jimenez-Molinos Francisco F   Wenger Christian C   Roldan Juan Bautista JB  

Frontiers in neuroscience 20230919

We characterize TiN/Ti/HfO<sub>2</sub>/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  ...[more]

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