Brain as an Emergent Finite Automaton: A Theory and Three Theorems

摘要

This paper models a biological brain—excluding motivation (e.g., emotions)—as a Finite Automaton in Developmental Network (FA-in-DN), but such an FA emerges incrementally in DN. In artificial intelligence (AI), there are two major schools: symbolic and connectionist. Weng 2011 [1] proposed three major properties of the Developmental Network (DN) which bridged the two schools: 1) From any complex FA that demonstrates human knowledge through its sequence of the symbolic inputs-outputs, a Developmental Program (DP) incrementally develops an emergent FA itself inside through naturally emerging image patterns of the symbolic inputs-outputs of the FA. The DN learning from the FA is incremental, immediate and error-free; 2) After learning the FA, if the DN freezes its learning but runs, it generalizes optimally for infinitely many inputs and actions based on the neuron’s inner-product distance, state equivalence, and the principle of maximum likelihood; 3) After learning the FA, if the DN continues to learn and run, it “thinks” optimally in the sense of maximum likelihood conditioned on its limited computational resource and its limited past experience. This paper gives an overview of the FA-in-DN brain theory and presents the three major theorems and their proofs.