There is a growing body of work that shows that both fuzzy and symbolic rule systems can be implemented using neural networks. This body of work also shows that these fuzzy and symbolic rules can be retrieved from these networks, once they have been learned by procedures that generally fall under the category of rule extraction. The paper argues that the idea of rule extraction from a neural network involves certain procedures, specifically the reading of parameters from a network, that are not allowed by the connectionist framework that these neural networks are based on. It argues that such rule extraction procedures imply a greater freedom and latitude about the internal mechanisms of the brain than is permitted by connectionism, but that such latitude is permitted by the recently proposed control theoretic paradigm for the brain. The control theoretic paradigm basically suggests that there are parts of the brain that control other parts and has far less restrictions on the kind of procedures that can be called `brain like.' The paper shows that this control theoretic paradigm is supported by new evidence from neuroscience about the role of neuromodulators and neurotransmitters in the brain. In addition, it shows that the control theoretic paradigm is also used in connectionist algorithms, although never acknowledged explicitly. The paper suggests that far better learning and rule extraction algorithms can be developed using these control theoretic notions and they would be consistent with the more recent understanding of how the brain works and learns.
ASJC Scopus subject areas
- Control and Systems Engineering
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics