On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.

The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disorders. In 2011, Osan et al. developed a computational model for exploring such phenomena based on attractor dynamics, Hebbian plasticity and synaptic degradation induced by prediction error. This model was able to explain, in a single formalism, experimental findings regarding the freezing behavior of rodents submitted to contextual fear conditioning. In 2017, through the study of inhibitory avoidance in rats, Radiske et al. showed that the previous knowledge of a context as non-aversive is a boundary condition for the reconsolidation of the shock memory subsequently experienced in that context. In the present work, by adapting the model of Osan et al. (2011) to simulate the experimental protocols of Radiske et al. (2017), we show that such boundary condition is compatible with the dynamics of an attractor network that supports synaptic labilization common to reconsolidation and extinction. Additionally, by varying parameters such as the levels of protein synthesis and degradation, we predict behavioral outcomes, and thus boundary conditions that can be tested experimentally.