In this paper we show that during the retrieval process in a binary symmetric Hebb neural network, spatially localized states can be observed when the connectivity of the network is distance-dependent and 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 retrieval and learning is found to be a sufficient condition to observe spatially localized retrieval states. The result is confirmed analytically and by simulation.
 Y. Roudi and A. Treves: “An associate network with spatially organized connectivity”, JSTAT, Vol. 1, (2004), P07010.
 K. Koroutchev and E. Korutcheva: Spatial asymmetric retrieval states in symmetric Hebb network with uniform connectivity, Preprint ICTP, Trieste, Italy, IC/2004/91, (2004), pp. 1–12.
 A. Anishchenko, E. Bienenstock and A. Treves: Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons, Los Alamos, 2005, http://xxx.lanl.gov/abs/q-bio.NC/0502003.
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SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.