Immunity to device variations in a spiking neural network with memristive nanodevices
Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but
are prone to device variability. We propose a novel neural network-based computing …
are prone to device variability. We propose a novel neural network-based computing …
Simulation of a memristor-based spiking neural network immune to device variations
We propose a design methodology to exploit adaptive nanodevices (memristors), virtually
immune to their variability. Memristors are used as synapses in a spiking neural network …
immune to their variability. Memristors are used as synapses in a spiking neural network …
Plasticity in memristive devices for spiking neural networks
Memristive devices present a new device technology allowing for the realization of compact
non-volatile memories. Some of them are already in the process of industrialization …
non-volatile memories. Some of them are already in the process of industrialization …
Fully memristive neural networks for pattern classification with unsupervised learning
Neuromorphic computers comprised of artificial neurons and synapses could provide a
more efficient approach to implementing neural network algorithms than traditional …
more efficient approach to implementing neural network algorithms than traditional …
A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation
Memristive devices represent a promising technology for building neuromorphic electronic
systems. In addition to their compactness and non-volatility, they are characterized by their …
systems. In addition to their compactness and non-volatility, they are characterized by their …
Tutorial: Concepts for closely mimicking biological learning with memristive devices: Principles to emulate cellular forms of learning
The basic building blocks of every neural network are neurons and their inter-cellular
connections, called synapses. In nature, synapses play a crucial role in learning and …
connections, called synapses. In nature, synapses play a crucial role in learning and …
Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights
AV Emelyanov, KE Nikiruy, AV Serenko… - …, 2019 - iopscience.iop.org
Neuromorphic systems consisting of artificial neurons and memristive synapses could
provide a much better performance and a significantly more energy-efficient approach to the …
provide a much better performance and a significantly more energy-efficient approach to the …
Spiking neural networks for inference and learning: A memristor-based design perspective
On metrics of density and power efficiency, neuromorphic technologies have the potential to
surpass mainstream computing technologies in tasks where real-time functionality …
surpass mainstream computing technologies in tasks where real-time functionality …
STDP and STDP variations with memristors for spiking neuromorphic learning systems
T Serrano-Gotarredona, T Masquelier… - Frontiers in …, 2013 - frontiersin.org
In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-
Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual …
Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual …
Memristors empower spiking neurons with stochasticity
M Al-Shedivat, R Naous… - IEEE journal on …, 2015 - ieeexplore.ieee.org
Recent theoretical studies have shown that probabilistic spiking can be interpreted as
learning and inference in cortical microcircuits. This interpretation creates new opportunities …
learning and inference in cortical microcircuits. This interpretation creates new opportunities …