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Updated: May 24, 2026
08:08
Odorant-induced Responses Recorded from Olfactory Receptor Neurons using the Suction Pipette Technique
Published on: April 5, 2012
Spontaneous olfactory receptor neuron activity determines follower cell response properties.
Joby Joseph1, Felice A Dunn, Mark Stopfer
1National Institutes of Health, National Institute of Child Health and Human Development, Bethesda, MD, USA.
Summary
Spontaneous neural activity in the locust olfactory system originates from olfactory receptor neurons (ORNs). This noise is managed by Kenyon cells (KCs) for effective signal detection.
Area of Science:
- Neuroscience
- Olfactory System Research
- Sensory Processing
Background:
- Neural systems commonly exhibit spontaneous activity, complicating signal detection.
- Understanding the origin and propagation of neural noise is crucial for sensory processing.
- The locust olfactory system provides a model for investigating noise in neural circuits.
Purpose of the Study:
- To determine the origin of spontaneous activity in locust olfactory projection neurons (PNs).
- To investigate how neural noise propagates and is modulated across olfactory processing stages.
- To identify mechanisms enabling signal detection amidst spontaneous neural activity.
Main Methods:
- Experimental investigation of spontaneous activity in locust olfactory neurons.
- Analysis of neural signal propagation from primary olfactory receptor neurons (ORNs) to higher processing centers.
- Computational simulation of the olfactory network to model signal discrimination.
Main Results:
- Spontaneous activity in PNs primarily originates from ORNs, not local antennal lobe circuits.
- ORN spontaneous activity tonically depolarizes target neurons (PNs, local neurons, and Kenyon cells).
- Differential response thresholds lead to high ORN/PN firing but near silence in KCs without odor stimulation.
Conclusions:
- The locust olfactory system effectively filters noise originating from ORNs.
- Kenyon cells (KCs) play a key role in discriminating signals from noise.
- Signal detection is optimized at points of maximal information convergence, such as within KCs.
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