Connectivity and dynamics in the olfactory bulb.
Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collectiv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009856 |
| _version_ | 1818138580112900096 |
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| author | David E Chen Kersen Gaia Tavoni Vijay Balasubramanian |
| author_facet | David E Chen Kersen Gaia Tavoni Vijay Balasubramanian |
| author_sort | David E Chen Kersen |
| collection | DOAJ |
| description | Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the connection probability between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. The model in turn predicts testable relationships between network structure and several functional properties, including lateral inhibition, odor pattern decorrelation, and LFP oscillation frequency. We use the model to explore the influence of cortex on the olfactory bulb, demonstrating possible mechanisms by which cortical feedback to mitral cells or granule cells can influence bulbar activity, as well as how neurogenesis can improve bulbar decorrelation without requiring cell death. Our methodology provides a tractable tool for other researchers. |
| first_indexed | 2024-12-11T10:14:27Z |
| format | Article |
| id | doaj.art-6ca09f8a8fc34adcb6fdf790ae614b14 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-11T10:14:27Z |
| publishDate | 2022-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-6ca09f8a8fc34adcb6fdf790ae614b142022-12-22T01:11:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-02-01182e100985610.1371/journal.pcbi.1009856Connectivity and dynamics in the olfactory bulb.David E Chen KersenGaia TavoniVijay BalasubramanianDendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the connection probability between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. The model in turn predicts testable relationships between network structure and several functional properties, including lateral inhibition, odor pattern decorrelation, and LFP oscillation frequency. We use the model to explore the influence of cortex on the olfactory bulb, demonstrating possible mechanisms by which cortical feedback to mitral cells or granule cells can influence bulbar activity, as well as how neurogenesis can improve bulbar decorrelation without requiring cell death. Our methodology provides a tractable tool for other researchers.https://doi.org/10.1371/journal.pcbi.1009856 |
| spellingShingle | David E Chen Kersen Gaia Tavoni Vijay Balasubramanian Connectivity and dynamics in the olfactory bulb. PLoS Computational Biology |
| title | Connectivity and dynamics in the olfactory bulb. |
| title_full | Connectivity and dynamics in the olfactory bulb. |
| title_fullStr | Connectivity and dynamics in the olfactory bulb. |
| title_full_unstemmed | Connectivity and dynamics in the olfactory bulb. |
| title_short | Connectivity and dynamics in the olfactory bulb. |
| title_sort | connectivity and dynamics in the olfactory bulb |
| url | https://doi.org/10.1371/journal.pcbi.1009856 |
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