As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power
consumption and powerful computing capability. However, the lack of effective learning …

There is a biological evidence to prove information is coded through precise timing of spikes
in the brain. However, training a population of spiking neurons in a multilayer network to fire …

With a similar capability of processing spikes as biological neural systems, networks of
spiking neurons are expected to achieve a performance similar to that of living brains …

Neuroscience research confirms that the synaptic delays are not constant, but can be
modulated. This paper proposes a supervised delay learning algorithm for spiking neurons …

Spiking neural networks are usually considered as the third generation of neural networks,
which hold the potential of ultra-low power consumption on corresponding hardware …

Recent developments in neural stimulation and recording technologies are providing
scientists with the ability of recording and controlling the activity of individual neurons in vitro …

Neural circuits respond to multiple sensory stimuli by firing precisely timed spikes. Inspired
by this phenomenon, the spike timing-based spiking neural networks (SNNs) are proposed …

Spiking neural networks (SNNs) are regarded as effective models for processing spatio-
temporal information. However, their inherent complexity of temporal coding makes it an …

There has been a lot of research on supervised learning in spiking neural network (SNN) for
a couple of decades to improve computational efficiency. However, evolutionary algorithm …