A pulse-driven learning network can be applied to any problem where adaptive behavior (i.e., the ability to adjust behavior to situations where a priori solutions are not known) is important. The pulse-driven learning network approach is different from other connectionist techniques in the way communication occurs between nodes. Since other connectionist techniques allow communication to occur in a continuum fashion, solutions at each compute cycle exist only when the system is in an equilibrium state. Not only is this a very computationally intensive process, but false solutions are also possible. The learning network does not have either of these problems because communication between nodes is in the form of a pulse and the correction solution is extracted from the network in as few as ten pulses from the input nodes. The results presented herein demonstrate the ability of a pulse-driven learning network to exhibit learning from association, learning from reward/punishment for simple problems and the existence of a stable solution for solving a complex problem.