This work lies within the scientific areas of Theory of Computation and artificial neural networks. It researches some possible knowledge bridges between both areas, and tries to integrate concepts in order to achieve a common and broader computational framework. This effort concentrates, firstly, on a computational architecture definition, based on a certain neural model (and subsequent demonstration that its power is equivalent to Turing Machines). Secondly, it will be developed a set of working tools to maximize and take advantage of this computational model. This will be achieved by using a high level programming language, and an automatic compilation process, able to translate the algorithmic representation of a certain problem into a parallel and modular neural network. These tools focus on two computational concepts: control and learning. In this context, control means all algorithms that use symbolic information, i.e., information with a well-defined context and meaning. Learning, on the other side, consists in a set of sub-symbolic algorithms, where there is no individual meaning for each basic piece of information, and all knowledge is distributed. It will also be presented a proposal for a specific hardware to execute the neural model neural, and a structure of a multiagent entity based on the previous concepts