Stabilization of stochastic recurrent neural networks via inverse optimal control

The paper studies the stabilization problem for a dynamic neural network disturbed by additive noise. The stabilization is achieved from the inverse optimal control approach, introduced in nonlinear control theory, using a quadratic Lyapunov function. A simple feedback control law is derived, which ensures that the neural network state is globally asymptotically stable in probability.