Neural Network Model of Memory Retrieval
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 re...
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Frontiers Media S.A.
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681782/ |
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pubmed-46817822016-01-05 Neural Network Model of Memory Retrieval Recanatesi, Stefano Katkov, Mikhail Romani, Sandro Tsodyks, Misha Neuroscience 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). Frontiers Media S.A. 2015-12-17 /pmc/articles/PMC4681782/ /pubmed/26732491 http://dx.doi.org/10.3389/fncom.2015.00149 Text en Copyright © 2015 Recanatesi, Katkov, Romani and Tsodyks. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Recanatesi, Stefano Katkov, Mikhail Romani, Sandro Tsodyks, Misha |
| spellingShingle |
Recanatesi, Stefano Katkov, Mikhail Romani, Sandro Tsodyks, Misha Neural Network Model of Memory Retrieval |
| author_facet |
Recanatesi, Stefano Katkov, Mikhail Romani, Sandro Tsodyks, Misha |
| author_sort |
Recanatesi, Stefano |
| title |
Neural Network Model of Memory Retrieval |
| title_short |
Neural Network Model of Memory Retrieval |
| title_full |
Neural Network Model of Memory Retrieval |
| title_fullStr |
Neural Network Model of Memory Retrieval |
| title_full_unstemmed |
Neural Network Model of Memory Retrieval |
| title_sort |
neural network model of memory retrieval |
| description |
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). |
| publisher |
Frontiers Media S.A. |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681782/ |
| _version_ |
1613513525929443328 |