Methuselah networks and optimal control

The authors describe a conceptual framework for optimal neural control called the Methuselah network. A cost surface constructed by the Methuselah network allows it to assess the performance of control schemes over long intervals. The network balances the cost of control with the need for exploratory control schemes. The environment or plant is controlled by a fixed-weight subnetwork whose weights are chosen to minimize long-term cost. The authors describe how an appropriate fixed-weight network can mimic adaptive-weight learning algorithms. This implies that the Methuselah network could be capable of developing learning algorithms