ArticleeLife2024
A neural network model of differentiation and integration of competing memories.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Hippocampal mechanisms underlying the resolution of competition in memory and perception.Nature communications · 2026Article
- Distinct Hippocampal Mechanisms Support Concept Formation and Updating.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Learning of visual sequences by neurons in the human hippocampus.bioRxiv : the preprint server for biology · 2026Article
- The Role of Rapid Eye Movement Sleep in Neural Differentiation of Memories in the Hippocampus.Journal of cognitive neuroscience · 2026Article
- Beyond Retrieval Competition: Asymmetric Effects of Retroactive and Proactive Interference in Associative Memory.Behavioral sciences (Basel, Switzerland) · 2025Article
- Targeting memory of embedded positive verbal content indirectly modulates negative visual memory.iScience · 2025Article
- Hippocampal transformations occur along dimensions of memory interference.bioRxiv : the preprint server for biology · 2025Article
- Age-related differences in the relationship between sustained attention and associative memory and Memory-Guided inference.Cognitive, affective & behavioral neuroscience · 2025Article
- Visual Statistical Learning Alters Low-Dimensional Cortical Architecture.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Differentiation of Related Events in Hippocampus Supports Memory Reinstatement in Development.Journal of cognitive neuroscience · 2025Article
- Inducing representational change in the hippocampus through real-time neurofeedback.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- INDUCING REPRESENTATIONAL CHANGE IN THE HIPPOCAMPUS THROUGH REAL-TIME NEUROFEEDBACK.bioRxiv : the preprint server for biology · 2023Article
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4 authors.
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Abstract
What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors - inactive memories are not modified, connections to moderately active competitors are weakened (leading to differentiation), and connections to highly active competitors are strengthened (leading to integration). The model also makes several novel predictions - most importantly, that when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. Overall, these modeling results provide a computational explanation for a diverse set of seemingly contradictory empirical findings in the memory literature, as well as new insights into the dynamics at play during learning.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.