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Related Concept Videos

Olfaction01:25

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

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A Proboscis Extension Response Protocol for Investigating Behavioral Plasticity in Insects: Application to Basic, Biomedical, and Agricultural Research
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Open-Source Automation of the Proboscis Extension Response Assay: From Odor Delivery to Deep Learning Behavior

Agustín E Lara1, Lautaro A Duarte1, Nicolás Pírez1

  • 1Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), CONICET-UBA, Buenos Aires, Argentina.

Current Protocols
|January 9, 2026
PubMed
Summary

This study introduces an automated honeybee proboscis extension response (PER) assay for studying olfactory learning. The new system enhances precision and reproducibility in behavioral neuroscience research.

Keywords:
DeepLabCutPERautomated behavioral analysisclassical conditioningolfactory learningproboscis extension response

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Area of Science:

  • Behavioral Neuroscience
  • Olfactory Learning
  • Insect Behavior

Background:

  • Honeybees (Apis mellifera) use olfaction for foraging and communication.
  • Traditional proboscis extension response (PER) assays for olfactory learning lack precision due to manual odor delivery and scoring.

Purpose of the Study:

  • To develop an automated PER assay for honeybees.
  • To improve precision, reproducibility, and throughput in olfactory learning studies.

Main Methods:

  • Utilized Arduino microcontrollers for automated odor delivery.
  • Employed Bonsai software for real-time synchronization and video recording.
  • Integrated DeepLabCut (DLC) for objective behavioral tracking of proboscis and antennal movements.
  • Designed and 3D printed custom hardware components for a cost-effective setup.

Main Results:

  • Achieved high-resolution, objective quantification of honeybee responses.
  • Demonstrated improved throughput, standardization, and analytical accuracy compared to manual methods.
  • Developed an open-source, modular system for behavioral neuroscience research.

Conclusions:

  • The automated PER assay offers a significant advancement for studying honeybee olfactory learning.
  • This cost-effective, replicable system enhances the objectivity and efficiency of behavioral analysis.
  • The open-source nature promotes wider adoption and further development in the field.