In neural networks, co-adaptation refers to when different hidden units in a neural networks have highly correlated behavior.

It is better for computational efficiency and the the model’s ability to learn a general representation if hidden units can detect features independently of each other.

A few different regularization techniques aim at reducing co-adapatation–dropout being a notable one.