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STDP and STDP variations with memristors for spiking neuromorphic learning systems
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 …
A review of current neuromorphic approaches for vision, auditory, and olfactory sensors
Conventional vision, auditory, and olfactory sensors generate large volumes of redundant
data and as a result tend to consume excessive power. To address these shortcomings …
data and as a result tend to consume excessive power. To address these shortcomings …
Optoelectronic synapse based on igzo‐alkylated graphene oxide hybrid structure
Recently, research interest in brain‐inspired neuromorphic computing based on robust and
intelligent artificial neural networks has surged owing to the ability of such technology to …
intelligent artificial neural networks has surged owing to the ability of such technology to …
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …
Neumann processor architectures is the way in which memory and processing is organized …
2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration
The hardware implementation of neuromorphic computing has attracted growing interest as
a promising candidate for confronting the bottleneck of traditional von Neumann computers …
a promising candidate for confronting the bottleneck of traditional von Neumann computers …
Pattern recognition using carbon nanotube synaptic transistors with an adjustable weight update protocol
Recent electronic applications require an efficient computing system that can perform data
processing with limited energy consumption. Inspired by the massive parallelism of the …
processing with limited energy consumption. Inspired by the massive parallelism of the …
Neuromorphic electronic circuits for building autonomous cognitive systems
Several analog and digital brain-inspired electronic systems have been recently proposed
as dedicated solutions for fast simulations of spiking neural networks. While these …
as dedicated solutions for fast simulations of spiking neural networks. While these …
A synergistic future for AI and ecology
Research in both ecology and AI strives for predictive understanding of complex systems,
where nonlinearities arise from multidimensional interactions and feedbacks across multiple …
where nonlinearities arise from multidimensional interactions and feedbacks across multiple …
Integration of nanoscale memristor synapses in neuromorphic computing architectures
Conventional neuro-computing architectures and artificial neural networks have often been
developed with no or loose connections to neuroscience. As a consequence, they have …
developed with no or loose connections to neuroscience. As a consequence, they have …
Hire-snn: Harnessing the inherent robustness of energy-efficient deep spiking neural networks by training with crafted input noise
Low-latency deep spiking neural networks (SNNs) have become a promising alternative to
conventional artificial neural networks (ANNs) because of their potential for increased …
conventional artificial neural networks (ANNs) because of their potential for increased …