  We study the recognition capabilities of the Hopfield model with auxiliary
hidden layers, which emerge naturally upon a Hubbard-Stratonovich
transformation. We show that the recognition capabilities of such a model at
zero-temperature outperform those of the original Hopfield model, due to a
substantial increase of the storage capacity and the lack of a naturally
defined basin of attraction. The modified model does not fall abruptly in a
regime of complete confusion when memory load exceeds a sharp threshold.
