Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

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 …

Recent research has shown the potential capability of spiking neural networks (SNNs) to
model complex information processing in the brain. There is biological evidence to prove the …

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of
primate and rodent. However, they highly simplify the computational properties of neurons …

Inspired by the dendritic and axonal morphology, some authors modeled the transmission
speed of action potentials through of fixed dendritic and axonal delays, respectively …

A common learning task for a spiking neuron is to map a spatio-temporal input pattern to a
target output spike train. There is no prescribed method for selection of the target output …

Based on the limbic system theory of mammalian emotional brain, supervised brain
emotional learning-based pattern recognizer (BELPR) has been recently proposed for multi …

The current assembly process of marine diesel engines is low in intelligence and the control
chart pattern classifier with unstable performance, which makes it difficult to control and …

За последние несколько лет методы глубокого обучения добились значительного
прогресса и стали широко распространёнными инструментами для решения …