Learning and imprinting in stationary and non-stationary environment

Abstract

The performance of the extended Bush-Mosteller learning and imprinting scheme developed previously is studied for stationary and non-stationary stochastic environments. As a performance criterion the average missing information level is chosen. For a stationary environment the approximate time course of the latter is derived and discussed, an exact symmetry in the performance of learning and imprinting schemes is proved, and the biological advantage of imprinting processes, with respect to energy consumption, is pointed out. For a non-stationary environment the performance of proper learning schemes is shown to be superior to imprinting processes, as the adaptability of the latter to novel environmental properties decreases exponentially in time. The optimal memory range of a learning system is calculated as a function of the time span during which the environment changes significantly and of the mean amplitude with which these changes occur.