Autobiographically Significant Concepts: More Episodic than Semantic in Nature? An Electrophysiological Investigation of Overlapping Types of Memory

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EEC: Learning to Encode and Regenerate Images for Continual Learning (2021, methodology): “Inspired by models of the hippocampus (Renoult et al., 2015), we use autoencoders to create compressed embeddings (encoded episodes) of real images and store them in memory.”
Centroid Based Concept Learning for RGB-D Indoor Scene Classification (2019, background): “CBCL is cognitively inspired from concept learning models of the hippocampus and the neo-cortex [14, 16, 18].”
Episodic Memory and Beyond: The Hippocampus and Neocortex in Transformation (2016, unknown): “A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript Renoult et al. (2015) used event-related potentials to explore this phenomenon further.”
Storing Encoded Episodes as Concepts for Continual Learning (2020, methodology): “To cope with this issue, we propose a process inspired by memory integration in the hippocampus and the neocortex (Mack et al., 2018; Renoult et al., 2015; Moscovitch et al., 2016).”
Cognitively-Inspired Model for Incremental Learning Using a Few Examples (2020, methodology): “In the hippocampal concept learning model [29, 40, 33], after the feature extraction step, the hippocampus calculates a term called the memory-based prediction error .”