Children’s Nutrition Research Center Staff Publications

Language

English

Publication Date

9-25-2024

Journal

The Journal of Neuroscience

DOI

10.1523/JNEUROSCI.0116-24.2024

PMID

39187379

PMCID

PMC11426377

PubMedCentral® Posted Date

8-26-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Recording and analysis of neural activity are 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 dropout 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 (RF) feature selection informed by Gini inequality (RF Gini impurity, 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 the odor offset. Together, our findings support a model of OB 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.

Keywords

Animals, Olfactory Bulb, Odorants, Mice, Male, Female, Mice, Inbred C57BL, Wakefulness, Smell, Olfactory Receptor Neurons, linear classifiers, mesoscale calcium imaging, odor information, olfactory bulb, random forest feature importance, redundancy

Published Open-Access

yes

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