iScienceNeurosciencePeer-reviewed journal article
Odor-evoked Increases in Olfactory Bulb Mitral Cell Spiking Variability
Cheng LyiD, Andrea K. BarreiroiD, Shree Hari GautamiD, Woodrow L. ShewiD
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- 4
- FWCI
- 0.42
- Field percentile
- top 47%
Abstract
At the onset of sensory stimulation, the variability and co-variability of spiking activity is widely reported to decrease, especially in cortex. Considering the potential benefits of such decreased variability for coding, it has been suggested that this could be a general principle governing all sensory systems. Here we show that this is not so. We recorded mitral cells in olfactory bulb (OB) of anesthetized rats and found increased variability and co-variability of spiking at the onset of olfactory stimulation. Using models and analysis, we predicted that these increases arise due to network interactions within OB, without increasing variability of input from the nose. We tested and confirmed this prediction in awake animals with direct optogenetic stimulation of OB to circumvent the pathway through the nose. Our results establish increases in spiking variability at stimulus onset as a viable alternative coding strategy to the more commonly observed decreases in variability in many cortical systems. SummaryThe spiking variability of neural networks has important implications for how information is encoded to higher brain regions. It has been well documented by numerous labs in many cortical and motor regions that spiking variability decreases with stimulus onset, yet whether this principle holds in the olfactory bulb has not been tested. In stark contrast to this common view, we demonstrate that the onset of sensory input can cause an increase in the variability of neural activity in the mammalian olfactory bulb. We show this in both anesthetized and awake rodents. Furthermore, we use computational models to describe the mechanisms of this phenomenon. Our finding establish sensory evoked increases in spiking variability as a viable alternative.
- organism: rat
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
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