Slow manifolds within network dynamics encode working memory efficiently and robustly.
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic is...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009366 |
| _version_ | 1819093997486866432 |
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| author | Elham Ghazizadeh ShiNung Ching |
| author_facet | Elham Ghazizadeh ShiNung Ching |
| author_sort | Elham Ghazizadeh |
| collection | DOAJ |
| description | Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits. |
| first_indexed | 2024-12-21T23:20:24Z |
| format | Article |
| id | doaj.art-2f274d172787454d833548af91935355 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T23:20:24Z |
| publishDate | 2021-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-2f274d172787454d833548af919353552022-12-21T18:46:48ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-09-01179e100936610.1371/journal.pcbi.1009366Slow manifolds within network dynamics encode working memory efficiently and robustly.Elham GhazizadehShiNung ChingWorking memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.https://doi.org/10.1371/journal.pcbi.1009366 |
| spellingShingle | Elham Ghazizadeh ShiNung Ching Slow manifolds within network dynamics encode working memory efficiently and robustly. PLoS Computational Biology |
| title | Slow manifolds within network dynamics encode working memory efficiently and robustly. |
| title_full | Slow manifolds within network dynamics encode working memory efficiently and robustly. |
| title_fullStr | Slow manifolds within network dynamics encode working memory efficiently and robustly. |
| title_full_unstemmed | Slow manifolds within network dynamics encode working memory efficiently and robustly. |
| title_short | Slow manifolds within network dynamics encode working memory efficiently and robustly. |
| title_sort | slow manifolds within network dynamics encode working memory efficiently and robustly |
| url | https://doi.org/10.1371/journal.pcbi.1009366 |
| work_keys_str_mv | AT elhamghazizadeh slowmanifoldswithinnetworkdynamicsencodeworkingmemoryefficientlyandrobustly AT shinungching slowmanifoldswithinnetworkdynamicsencodeworkingmemoryefficientlyandrobustly |