ArticleFrontiers in neural circuits2023
A deep network-based model of hippocampal memory functions under normal and Alzheimer's disease conditions.
Article in Frontiers in neural circuits, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Quercetin mitigates aluminum nanoparticle-induced neurotoxicity: a stereological and molecular study on memory, hippocampal integrity, and MAPK signaling.EXCLI journal · 2025Article
- A scoping review of mathematical models covering Alzheimer's disease progression.Frontiers in neuroinformatics · 2024Article
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Authors and funding
4 authors.
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Abstract
We present a deep network-based model of the associative memory functions of the hippocampus. The proposed network architecture has two key modules: (1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and (2) a module that computes familiarity of the stimulus and implements hill-climbing over the familiarity which represents the dynamics of the loops within the hippocampus. The proposed network is used in two simulation studies. In the first part of the study, the network is used to simulate image pattern completion by autoassociation under normal conditions. In the second part of the study, the proposed network is extended to a heteroassociative memory and is used to simulate picture naming task in normal and Alzheimer's disease (AD) conditions. The network is trained on pictures and names of digits from 0 to 9. The encoder layer of the network is partly damaged to simulate AD conditions. As in case of AD patients, under moderate damage condition, the network recalls superordinate words ("odd" instead of "nine"). Under severe damage conditions, the network shows a null response ("I don't know"). Neurobiological plausibility of the model is extensively discussed.
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