As key components of the human brain's neural network, synapses and neurons are
important processing units that enable highly complex neuromorphic systems. Spiking …

Neuromorphic devices are among the most emerging electronic components to realize
artificial neural systems and replace traditional complementary metal–oxide semiconductor …

Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability
to perform complex information processing has unfolded a new paradigm of computing to …

Ferroelectric tunnel junctions (FTJs) as the artificial synaptic devices have been considered
promising for constructing brain-inspired neuromorphic computing systems. However, the …

In recent years, biologically inspired systems, which emulate the nervous system of living
beings, are becoming more and more requested due to their ability to solve ill-posed …

Neuromorphic computation is one of the axes of parallel distributed processing, and
memristor-based synaptic weight is considered as a key component of this type of …

Artificial synapses are the fundamental of building a neuron network for neuromorphic
computing to overcome the bottleneck of the von Neumann system. Based on a low …

Artificial synapse having good linearity is crucial to achieve an efficient learning process in
neuromorphic computing. It is found that the synaptic linearity can be enhanced by …

Neuromorphic computing offers parallel data processing and low energy consumption and
can be useful to replace conventional von Neumann computing. Memristors are two-terminal …

Artificial neural network-based computing prospectively overcomes the von Neumann
bottleneck of conventional computers and significantly improves computational efficiency …