  We present a discrete-time formulation for the autonomous learning
conjecture. The main feature of this formulation is the possibility to apply
the autonomous learning scheme to systems in which the errors with respect to
target functions are not well-defined for all times. This restriction for the
evaluation of functionality is a typical feature in systems that need a finite
time interval to process a unit piece of information. We illustrate its
application on an artificial neural network with feed-forward architecture for
classification and a phase oscillator system with synchronization properties.
The main characteristics of the discrete-time formulation are shown by
constructing these systems with predefined functions.
