Life-long learning in incremental neural networks

This approach presents a possible solution to the stability-plasticity dilemma in incremental neural networks with a local insertion criterion. The main advantages are I) the capability of life-long learning, i.e., learning throughout the entire lifetime of a neural network, ii) stability in a stationary environment and iii) plasticity in a non-stationary environment, but only if the current knowledge does not fit the need of the task. Thus, the network structures its internal representation not like a copy of the environment but in order to fulfill the current task.