General transient length upper bound for recurrent neural networks

We show how to construct a Lyapunov function for a discrete recurrent neural network using the variable-gradient method, This method can also be used to obtain the Hopfield energy function. Using our Lyapunov function, we compute an upper bound for the transient length for our neural network dynamics. We also show how our Lyapunov function can provide insights into the effect that the introduction of self-feedback weights to our neural network has on the sizes of the basins of attraction of the equilibrium points of the neural network state space