  In this paper we show that during the retrieval process in a binary symmetric
Hebb neural network, spatial localized states can be observed when the
connectivity of the network is distance-dependent and when a constraint on the
activity of the network is imposed, which forces different levels of activity
in the retrieval and learning states. This asymmetry in the activity during the
retrieval and learning is found to be sufficient condition in order to observe
spatial localized states. The result is confirmed analytically and by
simulation.
