  One of the most interesting and still growing scientific fields is
neuromorphic engineering, which is focused on studying and designing hardware
and software with the purpose of mimicking the basic principles of biological
nervous systems. Currently, there are many research groups developing practical
applications based on neuroscientific knowledge. This work provides researchers
with a novel toolkit of building blocks based on Spiking Neural Networks that
emulate the behavior of different logic gates. These could be very useful in
many spike-based applications, since logic gates are the basis of digital
circuits. The designs and models proposed are presented and implemented on a
SpiNNaker hardware platform. Different experiments were performed in order to
validate the expected behavior, and the obtained results are discussed. The
functionality of traditional logic gates and the proposed blocks is studied,
and the feasibility of the presented approach is discussed.
