ArticleFrontiers in computational neuroscience2015
Neural Network Model of Memory Retrieval.
Article in Frontiers in computational neuroscience, 2015. 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.
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- A unified neural representation model for spatial and conceptual computations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Computational Framework for Memory Engrams.Advances in neurobiology · 2024Article
- Stochastic consolidation of lifelong memory.Scientific reports · 2022Article
- Free recall scaling laws and short-term memory effects in a latching attractor network.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- When shared concept cells support associations: Theory of overlapping memory engrams.PLoS computational biology · 2021Article
- Multiple-Timescale Neural Networks: Generation of History-Dependent Sequences and Inference Through Autonomous Bifurcations.Frontiers in computational neuroscience · 2021Article
- The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations.Frontiers in neural circuits · 2020Article
- Emergence of hierarchical organization in memory for random material.Scientific reports · 2019Article
- Article
- Itinerancy between attractor states in neural systems.Current opinion in neurobiology · 2016Review
- Memory recall and spike-frequency adaptation.Physical review. E · 2016Article
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Authors and funding
4 authors.
Funding
Abstract
Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al., 2013).
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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.