Memristive devices share remarkable similarities to biological synapses, dendrites, and
neurons at both the physical mechanism level and unit functionality level, making the …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

For a long time, biology and neuroscience fields have been a great source of inspiration for
computer scientists, towards the development of Artificial Intelligence (AI) technologies. This …

Spiking neural network (SNN) has drawn research interests as it mimics dynamic activities of
human brain and has the potential to perform real-time cognitive tasks. However, latency …

The performance of a biologically plausible spiking neural network (SNN) largely depends
on the model parameters and neural dynamics. This article proposes a parameter …

The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for
Tehran as a city built on geological faults using an artificial neural network—multi-layer …

In-memory computing based on non-volatile resistive memory can significantly improve the
energy efficiency of artificial neural networks. However, accurate in situ training has been …

The recent discovered spatial-temporal information processing capability of bio-inspired
Spiking neural networks (SNN) has enabled some interesting models and applications …

Spiking neural network (SNN) has attracted much attention due to its powerful spatio-
temporal information representation ability. Capsule Neural Network (CapsNet) does well in …

Compared to conventional artificial neural networks, spiking neural networks (SNNs) are
more biologically plausible and require less computation due to their event-driven nature of …