Optimal forgetting: Semantic compression of episodic memories

A generative model of memory construction and consolidation

Episodic memories are (re)constructed, share neural substrates with imagination, combine unique features with schema-based predictions and show schema-based distortions that increase with consolidation. Here we present a computational model in which hippocampal replay (from an autoassociative network) trains generative models (variational autoencoders) to (re)create sensory experiences from latent variable representations in entorhinal, medial prefrontal and anterolateral temporal cortices via the hippocampal formation. Simulations show effects of memory age and hippocampal lesions in agreement with previous models, but also provide mechanisms for semantic memory, imagination, episodic future thinking, relational inference and schema-based distortions including boundary extension. The model explains how unique sensory and predictable conceptual elements of memories are stored and reconstructed by efficiently combining both hippocampal and neocortical systems, optimizing the use of limited hippocampal storage for new and unusual information. Overall, we believe hippocampal replay training generative models provides a comprehensive account of memory construction, imagination and consolidation.

False memories for ending of events

Memories are not perfect recordings of the past and can be subject to systematic biases. Memory distortions are often caused by our experience of what typically happens in a given situation. However, it is unclear whether memory for events is biased by the knowledge that events usually have a predictable structure (a beginning, middle, and an end). Using video clips of everyday situations, we tested how interrupting events at unexpected time points affects memory of how those events ended. In four free recall experiments (1, 2, 4, and 5), we found that interrupting clips just before a salient piece of action was completed, resulted in the false recall of details about how the clip might have ended. We refer to this as "event extension." On the other hand, interrupting clips just after one scene had ended and a new scene started, resulted in omissions of details about the true ending of the clip (Experiments 4 and 5). We found that these effects were present, albeit attenuated, when testing memory shortly after watching the video clips compared to a week later (Experiments 5a and 5b). The event extension effect was not present when memory was tested with a recognition paradigm (Experiment 3). Overall, we conclude that when people watch videos that violate their expectations of typical event structure, they show a bias to later recall the videos as if they had ended at a predictable event boundary, exhibiting event extension or the omission of details depending on where the original video was interrupted. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Model of Semantic Completion in Generative Episodic Memory

Many studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this article, we present a model of the generative aspects of episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing parts based on general semantic information in a process we call semantic completion. The model combines two neural network architectures known from machine learning, the vector-quantized variational autoencoder (VQ-VAE) and the pixel convolutional neural network (PixelCNN). As episodes, we use images of digits and fashion items (MNIST) augmented by different backgrounds representing context. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch, and it generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally, we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong. This model contributes to a deeper understanding of the interplay between episodic memory and semantic information in the generative process of recalling the past.