Experience-dependent evolution of odor mixture representations in piriform cortex
Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mi...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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Public Library of Science (PLoS)
2023-04-01
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Series: | PLoS Biology |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129003/?tool=EBI |
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author | Alice Berners-Lee Elizabeth Shtrahman Julien Grimaud Venkatesh N. Murthy |
author_facet | Alice Berners-Lee Elizabeth Shtrahman Julien Grimaud Venkatesh N. Murthy |
author_sort | Alice Berners-Lee |
collection | DOAJ |
description | Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once. The neural representation of important features of the environment can change as an animal learns. This study shows that salient odor mixtures become over-represented and distinct in the olfactory cortex of mice, allowing a better and more robust categorization of these stimuli. |
first_indexed | 2024-04-09T15:22:31Z |
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institution | Directory Open Access Journal |
issn | 1544-9173 1545-7885 |
language | English |
last_indexed | 2024-04-09T15:22:31Z |
publishDate | 2023-04-01 |
publisher | Public Library of Science (PLoS) |
record_format | Article |
series | PLoS Biology |
spelling | doaj.art-5ee824b19d424d678a6031eaeece7d882023-04-29T05:30:52ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852023-04-01214Experience-dependent evolution of odor mixture representations in piriform cortexAlice Berners-LeeElizabeth ShtrahmanJulien GrimaudVenkatesh N. MurthyRodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once. The neural representation of important features of the environment can change as an animal learns. This study shows that salient odor mixtures become over-represented and distinct in the olfactory cortex of mice, allowing a better and more robust categorization of these stimuli.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129003/?tool=EBI |
spellingShingle | Alice Berners-Lee Elizabeth Shtrahman Julien Grimaud Venkatesh N. Murthy Experience-dependent evolution of odor mixture representations in piriform cortex PLoS Biology |
title | Experience-dependent evolution of odor mixture representations in piriform cortex |
title_full | Experience-dependent evolution of odor mixture representations in piriform cortex |
title_fullStr | Experience-dependent evolution of odor mixture representations in piriform cortex |
title_full_unstemmed | Experience-dependent evolution of odor mixture representations in piriform cortex |
title_short | Experience-dependent evolution of odor mixture representations in piriform cortex |
title_sort | experience dependent evolution of odor mixture representations in piriform cortex |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129003/?tool=EBI |
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