Identification of Exploration and Exploitation Balance in the Silkmoth Olfactory Search Behavior by Information-Theoretic Modeling

Abstract

Insects search for and find odor sources as their basic behaviors, such as when looking for food or a mate. This has motivated research to describe how they achieve such behavior under turbulent odor plumes with a small number of neurons. Among different insects, the silk moth has been studied owing to its clear motor response to olfactory input. In past studies, the “programmed behavior” of the silk moth has been modeled as the average duration of a sequence of maneuvers based on the duration of periods without odor hits. However, this model does not fully represent the fine variations in their behavior. In this study, we used silk moth olfactory search trajectories from an experimental virtual reality device. We achieved an accurate input by using optogenetic silk moths that react to blue light. We then modeled such trajectories as a probabilistic learning agent with a belief of possible source locations. We found that maneuvers mismatching the programmed behavior are related to larger entropy decrease, that is, they are more likely to increase the certainty of the belief. This implies that silkmoths include some stochasticity in their search policy to balance the exploration and exploitation of olfactory information by matching or mismatching the programmed behavior model. We believe that this information-theoretic representation of insect behavior is important for the future implementation of olfactory searches in artificial agents such as robots.

Multisensory-motor integration in olfactory navigation of silkmoth, Bombyx mori, using virtual reality system

“…Accordingly, there is a problem that the original search performance of living things cannot be replicated. For this reason, different kinds of research have been carried out more recently and applied to the bio-inspired algorithm, such as information-theoretic analysis of the trajectory of an insect ( Hernandez-Reyes et al, 2021 ), extraction of adaptability from neuroethology data by fuzzy inference ( Shigaki et al, 2020 ), and the acquisition of behavioral switching indices in response to environmental changes by measuring insect behavior while visualizing airflow ( Demir et al, 2020 ). In the current study, we found that behavioral modulation occurred based on the relationship between the direction of the odor arrival and the wind, and we used our data to reproduce a behavioral trajectory that was not only better than that of the conventional bio-inspired algorithm but was also more similar to the search behavior of the actual silkmoth.…”

Editorial: Biological and Robotic Inter-Limb Coordination

“…This behavior offers an interesting research topic in regard to motor-cognitive function: how is such successful locomotion achieved under conditions of turbulent odor plumes, utilizing the insects’ small number of neurons. Hernandez-Reyes et al (2021) measured the behavior of moths using a virtual reality system that presents accurate and reproducible odor stimuli by using blue light and optogenetic moths. Their results demonstrate that behavioral variations have a higher probability of obtaining more information than “programmed behaviors” (i.e., reactive, exploitative behaviors), suggesting that silk moths incorporate some stochasticity into their behavior in order to balance the exploration and exploitation of the acquired information.…”