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 …

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 …

In this paper we develop and analyze Spiking Neural Network (SNN) versions of Resilient
Propagation (RProp) and QuickProp, both training methods used to speed up training in …

Short-term load forecasting (STLF) is one of the planning strategies adopted in the daily
power system operation and control. All though many forecasting models have been …

The central keywords of this thesis are “verification” and “distribution”. Verification refers to
the process of finding, by formal means, design errors in complex hardware and software …

Liquid state machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs)
without training the SNN. Instead, LSMs randomly generate this network and then use it as a …

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 …

The main contribution of this letter is the derivation of a steepest gradient descent learning
rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model …

We investigate possibilities of inducing temporal structures without fading memory in
recurrent networks of spiking neurons strictly operating in the pulse-coding regime. We …

The aging of the voice, known as presbyphonia, is a natural process that can cause great
change in vocal quality of the individual. This is a relevant problem to those people who use …