Pirhayati, Delaram, Smith, Cameron L, Kroeger, Ryan, Navlakha, Saket, Pfaffinger, Paul, Reimer, Jacob, Arenkiel, Benjamin R, Patel, Ankit, Moss, Elizabeth H (August 2024) Dense and persistent odor representations in the olfactory bulb of awake mice. Journal of Neuroscience. e0116242024. ISSN 0270-6474
Abstract
Recording and analysis of neural activity is often biased toward detecting sparse subsets of highly active neurons, masking important signals carried in low magnitude and variable responses. To investigate the contribution of seemingly noisy activity to odor encoding, we used mesoscale calcium imaging from mice of both sexes to record odor responses from the dorsal surface of bilateral olfactory bulbs (OBs). The outer layer of the mouse OB is comprised of dendrites organized into discrete "glomeruli", which are defined by odor receptor-specific sensory neuron input. We extracted activity from a large population of glomeruli and used logistic regression to classify odors from individual trials with high accuracy. We then used add-in and drop-out analyses to determine subsets of glomeruli necessary and sufficient for odor classification. Classifiers successfully predicted odor identity even after excluding sparse, highly active glomeruli, indicating that odor information is redundantly represented across a large population of glomeruli. Additionally, we found that Random Forest feature selection informed by Gini Inequality (RFGI) reliably ranked glomeruli by their contribution to overall odor classification. RFGI provided a measure of "feature importance" for each glomerulus that correlated with intuitive features like response magnitude. Finally, in agreement with previous work, we found that odor information persists in glomerular activity after odor offset. Together, our findings support a model of olfactory bulb odor coding where sparse activity is sufficient for odor identification, but information is widely, redundantly available across a large population of glomeruli, with each glomerulus representing information about more than one odor.Significance statement This study leverages meso-scale imaging and machine learning to investigate how odor information is first represented in the brain. Typically, recordings of neuronal activity focus on active individual cells, potentially overlooking broader variations in neuronal responses across populations. Our results demonstrate that a considerable amount of olfactory information is redundantly distributed across a large proportion of olfactory bulb glomeruli. Even after excluding a majority of glomeruli, odor identification remained possible. These findings indicate that, although a few glomeruli are sufficient for odor recognition, an abundance of additional information is represented across a broad population. Understanding how the brain manages redundant olfactory information will shed light on its adaptive mechanisms for navigating diverse real-world circumstances and responding to fluctuating internal states.
| Item Type: | Paper |
|---|---|
| Subjects: | organism description > animal organism description > animal behavior organism description > animal > mammal organism description > animal > mammal > rodent > mouse organism description > animal behavior > odor recognition organism description > animal > mammal > rodent |
| CSHL Authors: | |
| Communities: | CSHL labs > Navlakha lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 26 August 2024 |
| Date Deposited: | 03 Sep 2024 13:13 |
| Last Modified: | 01 Oct 2024 17:48 |
| PMCID: | PMC11426377 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41647 |
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