The cellular architecture of memory modules in Drosophila supports stochastic input integration
The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2023-03-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/77578 |
| _version_ | 1797853175318839296 |
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| author | Omar A Hafez Benjamin Escribano Rouven L Ziegler Jan J Hirtz Ernst Niebur Jan Pielage |
| author_facet | Omar A Hafez Benjamin Escribano Rouven L Ziegler Jan J Hirtz Ernst Niebur Jan Pielage |
| author_sort | Omar A Hafez |
| collection | DOAJ |
| description | The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the fly’s center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit. |
| first_indexed | 2024-04-09T19:46:23Z |
| format | Article |
| id | doaj.art-53875d7a61d54b76b954c5a163ef0f4d |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-09T19:46:23Z |
| publishDate | 2023-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-53875d7a61d54b76b954c5a163ef0f4d2023-04-03T15:44:52ZengeLife Sciences Publications LtdeLife2050-084X2023-03-011210.7554/eLife.77578The cellular architecture of memory modules in Drosophila supports stochastic input integrationOmar A Hafez0https://orcid.org/0000-0002-7846-9226Benjamin Escribano1https://orcid.org/0000-0002-1432-5952Rouven L Ziegler2https://orcid.org/0000-0002-3050-7692Jan J Hirtz3https://orcid.org/0000-0002-4486-3057Ernst Niebur4https://orcid.org/0000-0002-2815-9262Jan Pielage5https://orcid.org/0000-0002-5115-5884Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, United StatesDivision of Neurobiology and Zoology, Department of Biology, University of Kaiserslautern, Kaiserslautern, GermanyDivision of Neurobiology and Zoology, Department of Biology, University of Kaiserslautern, Kaiserslautern, GermanyPhysiology of Neuronal Networks Group, Department of Biology, University of Kaiserslautern, Kaiserslautern, GermanyZanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, United States; Solomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, United StatesDivision of Neurobiology and Zoology, Department of Biology, University of Kaiserslautern, Kaiserslautern, GermanyThe ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the fly’s center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.https://elifesciences.org/articles/77578mushroom body output neurondendritic signal processingdecision makinglong term memoryneuronal output tuningshort term memory |
| spellingShingle | Omar A Hafez Benjamin Escribano Rouven L Ziegler Jan J Hirtz Ernst Niebur Jan Pielage The cellular architecture of memory modules in Drosophila supports stochastic input integration eLife mushroom body output neuron dendritic signal processing decision making long term memory neuronal output tuning short term memory |
| title | The cellular architecture of memory modules in Drosophila supports stochastic input integration |
| title_full | The cellular architecture of memory modules in Drosophila supports stochastic input integration |
| title_fullStr | The cellular architecture of memory modules in Drosophila supports stochastic input integration |
| title_full_unstemmed | The cellular architecture of memory modules in Drosophila supports stochastic input integration |
| title_short | The cellular architecture of memory modules in Drosophila supports stochastic input integration |
| title_sort | cellular architecture of memory modules in drosophila supports stochastic input integration |
| topic | mushroom body output neuron dendritic signal processing decision making long term memory neuronal output tuning short term memory |
| url | https://elifesciences.org/articles/77578 |
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