Artificial Neural Networks (ANNs) are often presented as powerful tools for data processing. Nevertheless,
ANNs need a theory and consequently an epistemological foundation. Usually scholars in this field
seem to believe that the brain theory is an implicit theory for ANNs. But, we still do not have a complete
brain theory, and the brain model for ANNs has been only an “inspiration” model. Further, a theory has to
explain a set of models or a set of phenomena internally. So, ANN lacks a theory. In this chapter the author
presents a theory of ANNs. Three levels of different complexity are created, in order to simulate a generative
path starting from the elementary units up to the more complex ones. The basic concepts of the first level are
the node and the connections. At the second level the main concept is the network. At the last level the concept
of an artificial organism is the key concept. Explicit rules govern the conversion from one level to another.
Further, at each level, semantic and syntactic components are considered. A theory of ANNs is needed
not only for didactic reasons but also for basic research: scholars need to see and consider the similarities and
differences among different ANNs from a mathematical, biological and philosophical point of view. A good
theory should increase the possibilities for planning, implementing and assessing necessary fundamental research.
Keywords: Artificial Neural Networks, Supervised Networks, Associative Memories, Autopoietic Networks.
