Tackling the stability/plasticity dilemma with double loop dynamic systems.
Christophe Lecerf · 1999
Abstract: Open and organized systems such as living organisms regulate their exchanges in order to maintain adaptation to their environment. When one reduces a biological organism to its central nervous system (CNS), adaptation comes up as an information flow exchange between the CNS and its environment. Though, the main mechanism used so far to explain learning is derived from the Hebb's hypothesis and it relies on structural modifications of the network through changing weights on connections. The double loop concept proposed here is the core of a structural and dynamic model tackling with incremental learning in large neural networks. A computer simulation of this concept is briefly described, then is given an equivalent mathematical dynamic system that is related to Thomas ' biological feedback theory. Due to the double loop architecture, the observed dynamics shows that the model gives a built-in functional answer to the stability/plasticity dilemma. 1.