SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Birgiolas, Justas; Jernigan, Christopher M.; Gerkin, Richard C.; Smith, Brian H.; Crook, Sharon

doi:10.3791/56803

Cited by 3 publications

(7 citation statements)

References 61 publications

(68 reference statements)

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“…The bees were restrained in harnesses that are commonly used in procedures for studying nonassociative, associative and operant conditioning in PER [ 28 ], and which allows for monitoring of antennal movements. Restrained honey bees actively scan the air flow around them with their antennae to sample headspace volatiles [ 29 ]. The patterns of these movements can be modified by appetitive conditioning [ 30 , 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…The patterns of these movements can be modified by appetitive conditioning [ 30 , 31 ]. Using video tracking [ 29 ], we recorded, frame-by-frame, the angular positions of each of the antennae in a 180° space on each side of the head. We recorded positions continuously in 4 sec blocks before, during, and after exposure to two odorants, 1-hexanol (hex) and 2-octanone (oct) ( Fig 1A ).…”

Section: Resultsmentioning

confidence: 99%

“…Honey bees were placed under a constant humidified air flow and vacuum system, in order to remove excess odorants. Bees were recorded from above with a web camera at 30 frames per sec as described by Birgiolas et al (2017) [ 29 ]. The experimental protocol follows that in the electrophysiological experiments with a few differences.…”

Section: Methodsmentioning

confidence: 99%

“…Behavioral videos were processed in SwarmSight [ 29 ] ( swarmsight.org ) in order to extract positions of antenna and proboscis and were exported to R (R Core Team 2017). Statistical analyses were used to compare the antennal movements of animals in three time-windows: 4 sec immediately before, 4 sec during, and 4 sec immediately after odor presentation.…”

Section: Methodsmentioning

confidence: 99%

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Novelty detection in early olfactory processing of the honey bee, Apis mellifera

et al. 2022

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Animals are constantly bombarded with stimuli, which presents a fundamental problem of sorting among pervasive uninformative stimuli and novel, possibly meaningful stimuli. We evaluated novelty detection behaviorally in honey bees as they position their antennae differentially in an air stream carrying familiar or novel odors. We then characterized neuronal responses to familiar and novel odors in the first synaptic integration center in the brain–the antennal lobes. We found that the neurons that exhibited stronger initial responses to the odor that was to be familiarized are the same units that later distinguish familiar and novel odors, independently of chemical identities. These units, including both tentative projection neurons and local neurons, showed a decreased response to the familiar odor but an increased response to the novel odor. Our results suggest that the antennal lobe may represent familiarity or novelty to an odor stimulus in addition to its chemical identity code. Therefore, the mechanisms for novelty detection may be present in early sensory processing, either as a result of local synaptic interaction or via feedback from higher brain centers.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Novelty detection in early olfactory processing of the honey bee, Apis mellifera

et al. 2022

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“…This showed how odor conditioning influenced the antennal motion pattern. Another study with markerless tracking of the antennae and proboscis tip used the large contrast between the tip and the background, which was detected via an algorithm that followed pixel-by-pixel average color changes [16]. Currently, the gold standard in animal tracking is the deep learning python toolbox DeepLabCut [17].…”

Section: Introductionmentioning

confidence: 99%

Associative Learning of Quantitative Mechanosensory Stimuli in Honeybees

Strelevitz,

Tiraboschi,

Haase

2024

Insects

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The proboscis extension response (PER) has been widely used to evaluate honeybees’ (Apis mellifera) learning and memory abilities, typically by using odors and visual cues for the conditioned stimuli. Here we asked whether honeybees could learn to distinguish between different magnitudes of the same type of stimulus, given as two speeds of air flux. By taking advantage of a novel automated system for administering PER experiments, we determined that the bees were highly successful when the lower air flux was rewarded and less successful when the higher flux was rewarded. Importantly, since our method includes AI-assisted analysis, we were able to consider subthreshold responses at a high temporal resolution; this analysis revealed patterns of rapid generalization and slowly acquired discrimination between the rewarded and unrewarded stimuli, as well as indications that the high air flux may have been mildly aversive. The learning curve for these mechanosensory stimuli, at least when the lower flux is rewarded, more closely mimics prior data from olfactory PER studies rather than visual ones, possibly in agreement with recent findings that the insect olfactory system is also sensitive to mechanosensory information. This work demonstrates a new modality to be used in PER experiments and lays the foundation for deeper exploration of honeybee cognitive processes when posed with complex learning challenges.

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Temporal dynamics of honeybee learning and decision-making are revealed by a novel automated PER system

Strelevitz

Tiraboschi

Haase

2023

Preprint

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The proboscis extension response (PER) has been widely used for decades to evaluate honeybees' (Apis mellifera) learning and memory abilities. This classical conditioning paradigm is traditionally administered manually, and produces a binary score for each subject depending on the presence or absence of the proboscis extension in response to a stimulus - typically an odor which has been associated with a sucrose reward - to classify whether or not the bee has learned the association. Here we present a fully automated PER system which delivers stimuli in a more controlled manner, and thus standardizes the protocol within and between labs; further, the AI-facilitated behavioral scoring reduces human error and allows us to extract a richer meaning from the outcome. The automated frame-by-frame assessment goes beyond the binary classification of "learned" or "not learned", expanding the possibilities for many other measures. Using this method, we investigate the real-time decision-making processes of honeybees faced with difficult learning tasks. When posed with a quantitative (rather than qualitative, as in the case of different odors) PER association, honeybees show a pattern of rapid generalization to both the rewarded and non-rewarded stimuli, followed by a slowly acquired discrimination between the two. Our work lays the foundation for deeper exploration of the honeybee cognitive processes when posed with complex learning challenges.

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SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Cited by 3 publications

References 61 publications

Novelty detection in early olfactory processing of the honey bee, Apis mellifera

Novelty detection in early olfactory processing of the honey bee, Apis mellifera

Associative Learning of Quantitative Mechanosensory Stimuli in Honeybees

Temporal dynamics of honeybee learning and decision-making are revealed by a novel automated PER system

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