The concept that neural information is encoded in the firing rate of neurons has been the
dominant paradigm in neurobiology for many years. This paradigm has also been adopted …

This paper surveys recent findings in neuroscience regarding the behavioral relevancy of
the precise timing with which real spiking neurons emit spikes. The literature suggests that in …

Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of
neural networks. Highly inspired from natural computing in the brain and recent advances in …

This second edition of the must-read work in the field presents generic computational
models and techniques that can be used for the development of evolving, adaptive modeling …

We demonstrate that spiking neural networks encoding information in the timing of single
spikes are capable of computing and learning clusters from realistic data. We show how a …

For a network of spiking neurons with reasonable postsynaptic potentials, we derive a
supervised learning rule akin to traditional error-back-propagation, SpikeProp and show …

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 …

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp,
for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed …

Abstract Spiking Neural Network (SNN) uses individual spikes in time field to perform as well
as to communicate computation in such a way as the actual neurons act. SNN was not …

Abstract Supervised learning in Spiking Neural Networks (SNN) is considered in this
dissertation. Spiking networks represent a special class of artificial neural networks, in which …