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Continuous attractor neural networks (CANN) form an appealing conceptualmodel for the storage of information in the brain. However a drawback of CANNis that they require finely tuned interactions. We here study the effect ofquenched noise in the interactions on the coding of positional informationwithin CANN. Using the replica method we compute the Fisher information for anetwork with position-dependent input and recurrent connections composed of ashort-range (in space) and a disordered component. We find that the loss inpositional information is small for not too large disorder strength, indicatingthat CANN have a regime in which the advantageous effects of local connectivityon information storage outweigh the detrimental ones. Furthermore, asubstantial part of this information can be extracted with a simple linearreadout.

Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity