Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to
multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and …

Spiking neural networks (SNNs), as the third generation of neural networks, are based on
biological models of human brain neurons. In this work, a shallow SNN plays the role of an …

The basal ganglia (BG), and more specifically the striatum, have long been proposed to play
an essential role in action-selection based on a reinforcement learning (RL) paradigm …

Biomimetic robotic fish that absorbs inspiration from fish has the advantage of high mobility,
high efficiency, and low noise. However, it is still challenging to make robotic fish adapt to …

In this work, our objective was to get a reliable model for predicting liquid density
ethanolwater and use it again later in modeling the ethanol production process from …

This paper explores machine learning using biologically plausible neurons and learning
rules. Two systems are developed. The first, for student performance categorisation, uses a …

Abstract Brain-computer interfaces (BCIs), transform neural signals in the brain into
instructions to control external devices. However, obtaining sufficient training data is difficult …

Liquid state machine (LSM) of spiking neurons is a biologically plausible computational
model imitating the structure and functions of the nervous system for information processing …

The hardware circuit of neural network based on forgetting memristors not only has the
characteristics of high computational efficiency and low power consumption, but also has the …