We propose a spiking neural network model that encodes information in the relative timing
of individual neuron spikes and performs classification using the first output neuron to spike …

During the past few decades, remarkable progress has been made in solving pattern
recognition problems using networks of spiking neurons. However, the issue of pattern …

For a network of spiking neurons that encodes information in the timing of individual spike
times, we derive a supervised learning rule, SpikeProp, akin to traditional error …

The activations of an analog neural network (ANN) are usually treated as representing an
analog firing rate. When mapping the ANN onto an equivalent spiking neural network (SNN) …

Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …

Gradient descent training techniques are remarkably successful in training analog-valued
artificial neural networks (ANNs). Such training techniques, however, do not transfer easily …

Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

In this review we focus our attention on supervised learning methods for spike time coding in
Spiking Neural Networks (SNNs). This study is motivated by recent experimental results …

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 …