Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
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Memory formation is a complex dynamic process. When memories are formed they are labile at first but gradually become progressively consolidated into stable traces via synthesis of new proteins. Recent rediscoveries show that after reactivation, a consolidated memory can become transiently sensitive to updating, enhancing or disruption and needs to undergo a process of restabilization, known as reconsolidation. The reconsolidation has challenged the classical view of how memories are consolidated over time and stored. The reconsolidation process is not fully understood, and theories about the nature and function of memory reconsolidation remain controversial. To reconcile many of the opposed views on reconsolidation, according to [43], a new theory of memory is required that would have to encompass: (i) the stabilization process after new learning, (ii) the stability of a non-reactivated memory, (iii) the lability of reactivated memory, and (iv) the boundary conditions for reconsolidation. We believe that the Theta-Regulated Attention Theory [62] and the "Neurolocator" model [25] as its implementation, can meet the above requirements. These theory and model are also able to answer the most difficult and unresolved outstanding questions of consolidation/reconsolidation studies.