Differential equations accompanying neural networks and solvable nonlinear learning machines

Solvable models of nonlinear learning machines are analyzed based on the theory of ordinary differential equations. It is shown that a function approximation neural network automatically extracts an accompanying differential equation from learning samples and that optimal parameters can be found without recursion procedures.