The spiking neural network (SNN) is regarded as a promising candidate to deal with the
great challenges presented by current machine learning techniques, including the high …

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the
major challenges of current machine learning techniques, including the high energy …

Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …

Neuromorphic computing is a promising technology that realizes computation based on
event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning …

Two-dimensional arrays of bi-component structures made of cobalt and permalloy elliptical
dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a …

Real-time detection of COVID-19 using radiological images has gained priority due to the
increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two …

Training of dendritic neuron model artificial neural networks is generally achieved by using
nonlinear least square methods. The distribution of random error terms is ignored in training …

Working memory is a fundamental feature of biological brains for perception, cognition, and
learning. In addition, learning with working memory, which has been show in conventional …

Image segmentation is an essential pre-processing step and is an indispensable part of
image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation …

It is widely believed the brain-inspired spiking neural networks have the capability of
processing temporal information owing to their dynamic attributes. However, how to …