A high throughput generative vector autoregression model for stochastic synapses
By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for large-scale simulations of computational architectures based on emerging devices is to accurately capture devi...
Main Authors: | Tyler Hennen, Alexander Elias, Jean-François Nodin, Gabriel Molas, Rainer Waser, Dirk J. Wouters, Daniel Bedau |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-08-01
|
Series: | Frontiers in Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.941753/full |
Similar Items
-
Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and Noisy Patterns
by: Christopher Bengel, et al.
Published: (2021-06-01) -
Tailor-made synaptic dynamics based on memristive devices
by: Christopher Bengel, et al.
Published: (2023-01-01) -
Electrode Material Dependence of Resistance Change Behavior in Ta<sub>2</sub>O<sub>5</sub> Resistive Analog Neuromorphic Device
by: Hisashi Shima, et al.
Published: (2018-01-01) -
Neuromorphic Computing Using Emerging Synaptic Devices: A Retrospective Summary and an Outlook
by: Jaeyoung Park
Published: (2020-09-01) -
Endurance of 2 Mbit Based BEOL Integrated ReRAM
by: Nils Kopperberg, et al.
Published: (2022-01-01)