In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Sustainable research on computational models of neuronal networks requires published
models to be understandable, reproducible, and extendable. Missing details or ambiguities …

The primate visual system has inspired the development of deep artificial neural networks,
which have revolutionized the computer vision domain. Yet these networks are much less …

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) using time-to-first-spike (TTFS) codes, in which neurons fire
at most once, are appealing for rapid and low power processing. In this theoretical paper, we …

Deep learning methods have made some achievements in the automatic skin lesion
recognition, but there are still some problems such as limited training samples, too …

Taking inspiration from the structure and behaviour of the human visual system and using
the Transposed Convolution and Saliency Map** methods of Convolutional Neural …

In this paper, we introduce a supervised learning algorithm, which avoids backward
recursive gradient computation, for training deep convolutional spiking neural networks …

Recently, spiking neural networks (SNNs) have been widely studied by researchers due to
their biological interpretability and potential application of low power consumption. However …

Traditionally the Perception Action cycle is the first stage of building an autonomous robotic
system and a practical way to implement a low latency reactive system within a low Size …