  To address the possible lack or total absence of pulses from particle
detectors during the development of its associate electronics, we propose a
model that can generate them without losing the features of the real ones. This
model is based on artificial neural networks, namely Generative Adversarial
Networks (GAN). We describe the proposed network architecture, its training
methodology and the approach to train the GAN with real pulses from a
scintillator receiving radiation from sources of ${}^{137}$Cs and ${}^{22}$Na.
The Generator was installed in a Xilinx's System-On-Chip (SoC). We show how the
network is capable of generating pulses with the same shape as the real ones
that even match the data distributions in the original pulse-height histogram
data.
