Noise to extract independent causes
Noisy threshold activation functions are used to force sparse responses on the output neurons of an unsupervised neural network enabling the network to identify the underlying independent factors of visual data. The addition of noise into the network enables us to control the response of the network to the data so we can force only as many outputs to respond to the data as there are signi cant factors in the data. Noise is also used to modularise the response of the network so that factors with temporal correlation may be coded in the same module of the output space.