Plasticity in inhibitory networks improves pattern separation in early olfactory processing
Abstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07879-2 |
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| _version_ | 1850184449490157568 |
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| author | Shruti Joshi Seth Haney Zhenyu Wang Fernando Locatelli Hong Lei Yu Cao Brian Smith Maxim Bazhenov |
| author_facet | Shruti Joshi Seth Haney Zhenyu Wang Fernando Locatelli Hong Lei Yu Cao Brian Smith Maxim Bazhenov |
| author_sort | Shruti Joshi |
| collection | DOAJ |
| description | Abstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee’s AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors. |
| format | Article |
| id | doaj-art-f0e8d51a139f4a4bab84d1c4c9d8f9c3 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-f0e8d51a139f4a4bab84d1c4c9d8f9c32025-08-20T02:17:02ZengNature PortfolioCommunications Biology2399-36422025-04-018111710.1038/s42003-025-07879-2Plasticity in inhibitory networks improves pattern separation in early olfactory processingShruti Joshi0Seth Haney1Zhenyu Wang2Fernando Locatelli3Hong Lei4Yu Cao5Brian Smith6Maxim Bazhenov7Department of Electrical and Computer Engineering, University of California San DiegoDepartment of Medicine, University of California San DiegoDepartment of Electrical, Computer and Energy Engineering, Arizona State UniversityFacultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencias, CONICETSchool of Life Science, Arizona State UniversityDepartment of Electrical and Computer Engineering, University of MinnesotaSchool of Life Science, Arizona State UniversityDepartment of Medicine, University of California San DiegoAbstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee’s AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors.https://doi.org/10.1038/s42003-025-07879-2 |
| spellingShingle | Shruti Joshi Seth Haney Zhenyu Wang Fernando Locatelli Hong Lei Yu Cao Brian Smith Maxim Bazhenov Plasticity in inhibitory networks improves pattern separation in early olfactory processing Communications Biology |
| title | Plasticity in inhibitory networks improves pattern separation in early olfactory processing |
| title_full | Plasticity in inhibitory networks improves pattern separation in early olfactory processing |
| title_fullStr | Plasticity in inhibitory networks improves pattern separation in early olfactory processing |
| title_full_unstemmed | Plasticity in inhibitory networks improves pattern separation in early olfactory processing |
| title_short | Plasticity in inhibitory networks improves pattern separation in early olfactory processing |
| title_sort | plasticity in inhibitory networks improves pattern separation in early olfactory processing |
| url | https://doi.org/10.1038/s42003-025-07879-2 |
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