Bavassi, LuzFuentemilla Garriga, Lluís2025-01-242025-01-242024-01-012472-1751https://hdl.handle.net/2445/217924The Segregation-to-Integration Transformation (SIT) model provides a framework for memory transformation based on changes in the neural network properties. SIT posits that memories shift from highly modular to less modular network forms over time, driven by neural reactivations, activation spread, and plasticity rules. The SIT model identified a critical period, shortly after memory formation, where reactivations can induce significant structural modifications. As repeated reactivation passes, the network becomes more stable and integrated, becoming more resistant to change, thus preserving the core information while reducing the likelihood of distortion or loss.16 p.application/pdfengcc-by (c) Bavassi, Luz et al., 2024http://creativecommons.org/licenses/by/3.0/es/MemòriaXarxes neuronals (Neurobiologia)MemoryNeural networks (Neurobiology)info:eu-repo/semantics/article2025-01-23info:eu-repo/semantics/openAccess39735504