Robust spatial memory maps encoded by networks with transient connections.
The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1006433 |
| _version_ | 1818716830847467520 |
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| author | Andrey Babichev Dmitriy Morozov Yuri Dabaghian |
| author_facet | Andrey Babichev Dmitriy Morozov Yuri Dabaghian |
| author_sort | Andrey Babichev |
| collection | DOAJ |
| description | The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity. |
| first_indexed | 2024-12-17T19:25:30Z |
| format | Article |
| id | doaj.art-361b229634e14986a19cb2b59f09b2f5 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-17T19:25:30Z |
| publishDate | 2018-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-361b229634e14986a19cb2b59f09b2f52022-12-21T21:35:24ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-09-01149e100643310.1371/journal.pcbi.1006433Robust spatial memory maps encoded by networks with transient connections.Andrey BabichevDmitriy MorozovYuri DabaghianThe spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.https://doi.org/10.1371/journal.pcbi.1006433 |
| spellingShingle | Andrey Babichev Dmitriy Morozov Yuri Dabaghian Robust spatial memory maps encoded by networks with transient connections. PLoS Computational Biology |
| title | Robust spatial memory maps encoded by networks with transient connections. |
| title_full | Robust spatial memory maps encoded by networks with transient connections. |
| title_fullStr | Robust spatial memory maps encoded by networks with transient connections. |
| title_full_unstemmed | Robust spatial memory maps encoded by networks with transient connections. |
| title_short | Robust spatial memory maps encoded by networks with transient connections. |
| title_sort | robust spatial memory maps encoded by networks with transient connections |
| url | https://doi.org/10.1371/journal.pcbi.1006433 |
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