  Artificial Intelligence has looked into biological systems as a source of
inspiration. Although there are many aspects of the brain yet to be discovered,
neuroscience has found evidence that the connections between neurons
continuously grow and reshape as a part of the learning process. This differs
from the design of Artificial Neural Networks, that achieve learning by
evolving the weights in the synapses between them and their topology stays
unaltered through time.
  This project has explored rules for growing the connections between the
neurons in Spiking Neural Networks as a learning mechanism. These rules have
been implemented on a multi-agent system for creating simple logic functions,
that establish a base for building up more complex systems and architectures.
Results in a simulation environment showed that for a given set of parameters
it is possible to reach topologies that reproduce the tested functions.
  This project also opens the door to the usage of techniques like genetic
algorithms for obtaining the best suited values for the model parameters, and
hence creating neural networks that can adapt to different functions.
