In many organisms, including humans, appropriate behavior is determined based on the integration of different kinds of information from the environment. Examples of information obtained from the environment include light, sound, odor, and wind. Unlike physical signals that are transmitted as waves, it is difficult to obtain precise directional and spatial information for odors because they (chemical substances) are carried by the wind. However, odors are widely utilized as a communication tool by organisms (Renou, 2014) because they have good residuality and diffusivity that physical wave signals do not have. Insects, in particular, communicate extensively using odor (e.g. aggregation pheromones, trail pheromones, sex pheromones; Wyatt, 2014), despite their small-scale neural systems. Odor information is also largely used to locate feeding sites and flowers (Renou, 2014).

Understanding odor-based search behavior is of great value not only in biology, but also in engineering research. This is because odor-based search can be applied to the use of gas leak source search robots or lifesaving robots in a disaster area instead of dogs. Such odor-based search behavior has been reported to be observed in walking and flying insects such as cockroaches, beetles, moths, and flies, and detailed behavioral analyses have also been conducted (Willis et al., 2011; Burkhardt et al., 1967; Ryohei et al., 1992; Willis and Arbas, 1991; Saxena et al., 2018; Pang et al., 2018). For example, flying moths (Manduca sexta L.) or cockroaches (Periplaneta americana) move upwind when they encounter an odor, and move in a crosswind direction when they lose the odor. Moreover, walking moths (Bombyx mori) and dung beetles (stercorarius) perform a ‘recapture’ or ‘reenter’ of the odor plume by combining straight and zigzag movements. They have excellent performance in searching for feeding grounds, nests, and mating partners using this olfactory behavior (Reisenman et al., 2016). Although there have been many attempts to model the excellent odor-source search behavior of insects and implement them in robots (Chen and Huang, 2019), artificial systems have not yet achieved the same abilities as insects. One of the reasons for this lack of performance is that models do not incorporate when and under what conditions and which sensory information is fed back to inform subsequent behavior. For example, in order to move into an environment with high uncertainty, an agent needs to modulate its speed of movement according to the type and amount of sensory input, but many models have insufficient discussion on speed modulation. To solve this problem, it is necessary to measure behavioral changes in insects when multiple types of sensory information are presented to them. Previously, Pansopha et al., 2014 found that the mating behavior of a silkmoth (elicited by odor stimuli) was modulated by visual stimuli. Further, in a study on crickets, Haberkern and Hedwig, 2016 reported that long-term tactile stimulation suppressed phototaxis, suggesting that behavioral switching between proximal environmental information and phototaxis may occur. Duistermars and Frye, 2008 showed that by providing visual stimuli using optical flows to Drosophila in addition to odor stimuli, spatial information, such as the orientation of the odor source, could be obtained. These results reveal that behavioral modulation and switching mechanisms occur in insects through the acquisition of multiple types of environmental information. Because these experimental results were obtained when controlled stimuli were provided, the mechanisms of behavioral modulation and switching when complex environmental changes are presented are still unknown. Because the search behavior of insects is expected to depend on the environmental dynamics of the search, there should be a difference between search behavior in a relatively well-organized airflow environment and a turbulent environment. However, it is difficult to obtain the relationship between the sensory input and behavioral output of insects moving in a turbulent environment (Baker et al., 2018).

In recent years, the use of virtual reality (VR) systems in insect behavior experiments has attracted attention as a way of presenting complex environmental changes. Generally speaking, VR is a system to achieve (or measure) a purposeful behavior by connecting a device that can provide multisensory stimuli to the organisms and a space in which the organisms can move virtually (Zhou and Deng, 2009). Based on the general definition of VR, previous experiments in which stimuli were presented to multi-sensory organs of an organism did not provide a space for the organism to virtually perform the task. Therefore, even if we could measure the behavioral changes when stimuli were input to multi-sensory organs, it would be difficult to directly discuss which part of the task the behavioral changes were contributing to. By converting the behavioral experiment of the organisms into VR, we expect to clarify how the behavioral modulation mechanism using multisensory information contributes to the function of navigation. VR has therefore been proposed to investigate the navigation of mammals and invertebrates (e.g., Naik et al., 2020; Radvansky and Dombeck, 2018). For example, Kaushik et al., 2020 found, using an insect VR system, that dipterans use airflow and odor information for visual navigation. Moreover, Radvansky and Dombeck, 2018 succeeded in measuring the olfactory navigation behavior of mammals (mice) using a VR system. All motile organisms use spatially distributed chemical features of their surroundings to guide their behavior; however, investigating the principles of this behavioral elicitation has been difficult because of the technical challenges in controlling chemical concentrations in space and time during behavioral experiments. Moreover, their research has demonstrated that the introduction of VR into the olfactory navigation of organisms can solve the above problem. Thus, VR systems allow for a more natural presentation of environmental changes, as well as a quantitative analysis of the effects of motion modulation and switching mechanisms on functions such as search and navigation. Furthermore, they allow researchers to create and test insects in situations that do not occur in nature and may therefore play an important role in the construction of robust behavioral decision algorithms for unknown environments.

In order to elucidate the adaptive odor-source search behavior of insects, we constructed a VR system that can present multiple types of environmental information simultaneously and continuously, and we employed it to clarify how sensory information other than odor is used. Our VR system was connected to a virtual field built in silico and presented odor, wind, and visual stimuli according to environmental changes in the virtual search field. We employed an adult male silkmoth (Bombyx mori) as our measurement target. Female search behavior of the silkmoth has a stereotypic pattern (Ryohei et al., 1992), but the duration and speed of the behavior are regulated by the frequency of odor detection and the input of other sensory information (Pansopha et al., 2014; Shigaki et al., 2019; Shigaki et al., 2020). However, previous studies have observed silkmoth behavior in response to a specified, unchanging amount of the stimulus, and the nature of behavioral changes during an actual odor source search warrants further investigation.

In this study, we used a VR system to clarify which sensory organs the silkworm moth uses to search for females and how they are used. In addition, we constructed a model from our biological data and tested the validity of the model using a constructive approach.

In this study, we analyzed changes in the behavior of an adult male silkmoth in his search for females in response to multiple sensory inputs. To measure behavior, we constructed a novel virtual reality device that presents odor, visual, and wind stimuli (Figure 1A) to provide the silkmoth with the illusion that it was searching for a female (see Figure 1—video 1). We provided the silkmoth with odor, wind, and visual stimuli (Figure 1B). In order to provide the odor stimulus to the left and right antennae independently, we designed two odor discharge ports above each antenna. We integrated these discharge ports with a tethered rod that was fixed to the silkmoth body. We provided the wind stimulus to the silkmoth from four directions: front, back, left, and right. Moreover, the visual stimulus provided an optical flow in the direction opposite to the turn direction of the silkmoth. In this experiment, we focused on odor-based navigation instead of visual object recognition, so that we adopted an optical flow to show which direction the landscape was flowing. The VR device was connected to a virtual field built in silico, and the odor puffs in the virtual field were produced by image processing of the actual odor diffusion using an airflow visualization system (Yanagawa et al., 2018; Connor et al., 2018). In addition, the virtual field was set with wind flowing uniformly from left to right. The search performance of the silkmoth using the constructed VR system was the same as that in free-walking experiments (see Supplementary Materials). We carried out experiments using the VR by changing the number of sensory inputs with (1) two or less types of sensory input (Group 1: odor only/odor and wind/odor vision), and (2) three types of sensory input (Group2: odor wind vision) (Figure 1C). Three repetitions of the experiment were conducted using 10 silkworm moths for each environmental condition ($n$ = 30). We set a time limit of 300 s because theoretically, the silkmoth could reach the odor source in an infinite time. The search was considered a failure if the moth did not enter a radius of 10 mm from the odor source within the time limit.

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Wind and visual effects on the olfactory navigation

We compared the search performance in response to different types of stimuli by presenting wind and visual stimuli in addition to olfactory stimuli in all conditions. We first measured the behavior when wind or visual stimuli were provided in addition to an odor stimulus (Group 1). The blue and black lines in Figure 2B—F are the trajectories of successful and unsuccessful searches, respectively. The amplitude of the heading angle histogram (Figure 2B—F; displayed in polar coordinates) indicates the frequency. Judging from the trajectory and heading angle histogram, the trajectories in cond. 1 (only odor) show that although the silkmoth moved toward the upwind direction with a high frequency, it deviated from the range where the odor had a high probability of reaching (high odor area), and the search failed (Figure 2B). By presenting wind (cond. 2) and visual (cond. 3) information in addition to odor, the silkmoth reduced the deviations from the high odor area, which qualitatively indicates that the odor-source search behavior was modulated by other sensory information (Figure 2CD). In the case of wind input, even if there was a deviation, modulation was elicited to change the heading angle to the upwind direction. In the case of visual input, although there was no trajectory deviating away from the high-odor area, because the frequency of the heading angle histogram was also high at angles other than $0^{\circ }$, the posture control makes the search behavior more careful. According to the heading angle histogram of cond. 4, which was presented from the direction opposite to the direction in which the wind actually blew, there was also a peak in the downwind direction; This indicates that the behavioral modulation was affected by the wind information (Figure 2E). Even when the visual stimulus direction was reversed, the peak in the upwind direction was not extreme, and a peak was generated in the crosswind direction (Figure 2F). By presenting the visual information in the opposite direction (the visual information was input as if it were rotating in the opposite direction), the illusion is that it was not rotating correctly, and more rotational behavior was induced. In order to quantitatively evaluate how similar these search trajectories were for each condition, we performed a two-dimensional histogramization of the trajectories and a calculation of similarity.

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We created a migration probability map in order to visualize the effects of differences in environmental conditions on the behavioral trajectory (Figure 3A—E). To quantitatively evaluate the similarity between the migration probability maps, we calculated the earth mover’s distance (EMD) (Rubner et al., 1997). EMD is an index that calculates whether the distributions of two histograms are similar. The smaller the EMD value, the more similar the two histograms; the larger the value, the less similar they are. Figure 4F shows the result of calculating EMD based on cond. 1, in which only the odor stimulus was presented. The silkmoths’ trajectories in conds. 3 and 5 (odor + vision) were very similar to the trajectory in cond. 1 (only odor), suggesting that vision did not significantly affect search behavior. In addition, the EMD value when wind stimulus was presented in addition to odor was 10, suggesting that the addition of wind stimulus significantly affected search behavior. We illustrated the navigation success rate and search time (yellow background in Figure 4AB). Additionally, we calculated the search success rate per unit time (SPT) as a measure of the relationship between the search success rate and search time (yellow background in Figure 4C). Higher SPT values represent better search performance. The success rate was most improved when the wind was presented correctly (cond. 2) compared to when only the odor stimulus was provided (cond. 1). The success rate was lower under the condition where the wind was presented from the direction opposite to the direction detected in the virtual environment (cond. 4), compared with the condition where only the odor stimulus was presented (cond. 1). Moreover, SPT in conds 3 and 5 (in which the direction of the visual stimulus was changed without presenting the wind stimulus) was almost the same (0.411 vs 0.409), suggesting that the visual stimulus had little effect on search performance. Therefore, the success rate of navigation tends to improve by correctly presenting a wind stimulus in addition to the odor.

Figure 3

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Figure 4

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We found that when three types of sensory stimuli were presented (Group 2), the success rate changed significantly compared to when two types of sensory stimuli were presented (red background in Figure 4). Focusing on cond. i, the SPT values were also significantly different, suggesting that olfactory navigation can be performed more accurately and efficiently by presenting all sensory stimuli. Additionally, under the condition that the wind stimulus is presented from the direction opposite to the direction received in the virtual environment, the search success rate is significantly reduced, and it is clear that information on wind direction is an important factor. We therefore hypothesized that the silkmoth performs efficient female searches using all sensory inputs: odor, wind, and vision.

Extraction of behavioral modulation mechanisms in the odor-source search

In the previous section, it was found that information on wind direction, in addition to odor, contributed to improving the success rate in searching for the odor source. Here, we analyze in detail how behavior is modulated by visual and wind information using three experimental conditions: a forward condition (cond. i), an inverse condition for wind direction information (cond. ii), and an inverse condition for visual stimuli (cond. iii).

We first analyzed the effect of information on wind direction on odor-source search behavior. Specifically, we analyzed whether the movement speed changed between cond. i and cond. ii because the wind causes behavioral modulation. We calculated the movement speed change with respect to the odor detection frequency (Hz) because it has been reported that the odor detection frequency is related to the distance from the odor source (Kikas et al., 2001; Figure 5AB). Here, odor detection frequency is defined as the frequency at which the silkmoth receives odors per unit of time. The odor detection frequency at each point of the odor field in this study is listed in the Supplementary Materials (Figure 1—figure supplement 1). The velocity distribution at each odor detection frequency is represented by a box plot. The red box plots in each figure represent the maximum values, and the circled dots in each box plot are the average values. The dotted line in Figure 5A/B is the result of performing a least-square approximation on the average value. The result of this least-square approximation is utilized in the mathematical modeling. According to the results of the speed change of cond. i, the translational speed increases monotonically up to the odor detection frequency of 0.7 Hz and decreases after 0.7 Hz. Moreover, the angular velocity increases monotonically up to 0.4 Hz, and then decreases. Considering that the pheromone release frequency of the silk moth female is about 0.8 Hz, when the wind is received from the same direction as the odor, the male silkmoth may actively move until it approaches the pheromone release frequency. At higher frequencies ( > 0.8 Hz), the male silkmoth reduces its movement speed to facilitate a mate search because there is a possibility that the female is nearby.

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According to the results of the translational speed of cond. ii, the moth always moves at a constant speed regardless of the odor detection frequency. We also found that the frequency at which the maximum value occurs for the angular velocity was shifted to 0.2 Hz. This may be because the peak of the angular velocity shifts toward a lower odor detection frequency, and if the odor and wind direction do not match, the silkmoth rotates slowly to carefully search for the odor. This is probably because if the odor and wind direction do not match, the odor is carefully searched for by slowing down the overall movement speed.

Next, we investigated the effects of visual stimuli on odor-source search behavior. Section 2.1 suggests that visual stimuli do not directly contribute to search performance. However, many flying insects with compound eyes use visual information for their own postural control (Dyhr and Higgins, 2010; Dyhr et al., 2013). Because the silkmoth, which may retain some vestiges of flying insects (Kanzaki, 1998; Shigaki et al., 2016), also changes its rotational behavior in response to a visual stimulus presented as an optical flow (Pansopha et al., 2014), it is possible that the silkmoth utilizes the visual information as the input amount for postural control. To test this hypothesis, we compared the angular velocities of silkmoths in cond. i (all stimuli in the forward direction) to those in cond. iii (visual stimuli presented in the inverse direction). Figure 5C shows a histogram of the left and right angular velocities under each experimental condition. The red color in Figure 5C indicates the angular velocity when rotating counterclockwise, and the blue color shows the angular velocity when rotating clockwise. The position of the vertical bar above the histogram represents the average angular velocity, and the length of the horizontal bar represents the standard deviation. Although the average angular velocities of the left and right rotations are the same in cond. i (visual stimuli presented correctly), the angular velocities of the left and right rotations differed when the visual stimuli were presented in the opposite direction to reality (Wilcoxon rank sum statistical test, p < 0.05). These results indicate that silkmoths, like flying insects, use visual stimuli for postural control.

We found that the silkmoth modulated its behavior based on whether or not the direction of odor and wind detection coincided.

Modeling and validation of behavioral modulation mechanisms

Here, we investigated how behavioral modulation informed by behavioral experiments using a VR system contributes to the odor-source search. A silkmoth moves by walking on a two-dimensional plane with six legs, but it does not move in a lateral direction. Therefore, we assumed that it has non-holonomic constraints and constructed our model to output straight-ahead and rotational movements. Previous studies have proposed a silkmoth search model called the surge-zigzagging algorithm (Ryohei et al., 1992; Shigaki et al., 2020), which makes action decisions using only olfactory information. In this study, we updated the surge-zigzagging algorithm to incorporate wind direction information. For convenience, we called this algorithm MiM2 (the multisensory input-based motor modulation) algorithm. A block diagram of the constructed MiM2 algorithm is presented in Figure 6A. The model has a straight-ahead speed (v) controller (Equation (1)) and an angular velocity () controller (Equation 2), and each controller changes output depending on the amount of sensory input. The detailed equations for each controller are shown below:

(1) ${\displaystyle v(t)=\frac{K_v(f)\times \exp (-(t-t_d))}{1+\exp (\gamma ((t-t_d)-\beta ))}}$

(2) ${\displaystyle \omega (t)=\frac{{\omega }_0\times R_d(0)}{1+\exp (\gamma ((t-t_d)-\beta ))}+\frac{K_{\omega }(f)\times R_d(N)}{1+\exp (-\gamma ((t-t_d)-\beta ))}}$

(3) ${\displaystyle K_{v/\omega }(f)=a_{v/\omega }\times f+b_{v/\omega }}$

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The free parameters were set to $\gamma =1000$, $\beta =0.50$, and ${\omega }_0=0.57$. Here, $f$ denotes the odor detection frequency. Moreover, $K(f)$ is a linear function that varies with the direction of odor and wind detection. The parameters a and b of $K(f)$ are shown in Table 1. These were obtained by approximating the results of the behavioral experiment using the least-squares method (dotted line in Figure 5A/B). In addition, t_d, $R_d$, and $N$ represent the odor detection timing, the odor detection direction, and the number of turn motions, respectively; $R_d(0)$ represents a straight-ahead motion state and takes a value of –1 when the left antenna detects and one when the right antenna detects; if both antennae detect, a value of –1 or one is randomly selected; $N$ increases according to Equation (4) up to four, but does not take a value of four or more because the number of zigzagging motions is approximately three (Ryohei et al., 1992). When it receives an odor stimulus according to Equations (1–3), it generates a straight motion for 0.5 s and then makes a turn motion.

(4) ${\displaystyle N(t)=[0.0116(t-t_d{)}^3-0.199(t-t_d{)}^2+1.1971(t-t_d)+0.4482]}$

Table 1

(a) $k_v$		$a_v$	$b_v$
Odor direction = Wind direction	$f$≤0.7	5.36	12.9
	$f$>0.7	–11.4	24.6
Odor direction ≠ Wind direction	$f$≤0.7	0.0	10.5
	$f$>0.7	0.0	10.5
(b) $k_{\omega }$		$a_{\omega }$	$b_{\omega }$
Odor direction = Wind direction	$f$≤0.4	0.638	1.40
	$f$>0.4	–1.22	2.16
Odor direction ≠ Wind direction	$f$≤0.4	0.870	1.42
	$f$>0.4	–0.997	1.80

By passing through the directional discriminator and frequency counter, the odor is converted into information such as whether it was detected by the left or right antennae and how much odor it was exposed to. The wind is converted into wind direction information. A comparator is used to determine whether the direction of odor detection and the direction of the wind are the same, and the results are input to the straight-ahead speed and angular velocity controllers, which also receive odor detection frequency. Visual information controls the angular velocity of the left and right rotational movements, following which olfactory behavior takes place. From this information, the movement speed output is calculated, and the behavior is generated. We compared the MiM2 algorithm to the previous surge-zigzagging algorithm and the casting algorithm, which uses wind information for searching in a moth-inspired algorithm (Li et al., 2016).

The simulation environment employed a virtual environment similar to that used in the behavioral experiments in the VR system (see Figure 6—video 1). For each algorithm, we performed 1000 odor-source search experiments to evaluate search success rate and trajectory. In the simulation experiment, we set two scenarios to verify each algorithm. One was the same initial position (x, y) = (300, 0) [mm] as in the biological experiment, and the other was the initial position moved ± 100 mm in the crosswind direction (x, y) = (300, ± 100) [mm]. Because the latter was near the edge of the area where the odor reaches, deviation behavior possibly occur frequently. Figure 6B and C show the search success rate and the search time, respectively. The MiM2 algorithm showed a higher search success rate than the other algorithms, regardless of the initial position (Fisher’s exact test, p < 0.01). The casting algorithm showed the shortest search time (Steel-Dwass test, p < 0.01) because it is an algorithm that actively moves upwind using odor detection. However, if the heading angle when moving upwind was incorrect, the search success rate of this algorithm was the lowest of the three, because there was a high possibility that the agent would have moved in the wrong direction. Next, we focused on the trajectory and heading angle change shown in Figure 6D—L. The blue and black lines in Figure 6D—I are the trajectories of successful and unsuccessful searches, respectively. Moreover, the amplitude of the heading angle histogram (Figure 6J—L; displayed in polar coordinates) indicates the frequency. For the readability of the trajectory and heading angle histogram, we randomly selected 100 trials of the 1000 replicates and plotted them. Regardless of the initial position, the search trajectory indicates that the MiM2 algorithm always places the searching agent in the middle of the odor distribution, whereas the conventional surge-zigzagging and surge-casting algorithms tend to place the searching agents near the edges of the odor. According to the heading angle histogram, the MiM2 algorithm resembles the actual silkmoth (Figure 2), but the other algorithms produced completely different histograms.

In order to quantitatively evaluate the trajectory of 1,000 simulations, we visualized the trajectory data (Figure 7A—F). The black line in Figure 7A—F shows the range that the odor has a high probability of reaching. Moreover, we evaluated the differences in trajectory between algorithms quantitatively by calculating their EDMs (Figure 7H). The comparison of these EDM values suggests that the MiM2 algorithm most closely resembles the actual silkmoth movements (Figure 7G) and was the closest to the results of behavioral experiments of the silkmoths while free walking. Thus, the MiM2 algorithm can simulate search behavior similar to that of real silkmoths by modulating the movement speed based on wind information in addition to odor, and equalizes the left-right angular velocities based on visual information.

Figure 7

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All experimental data are available on Zenodo via https://doi.org/10.5281/zenodo.5724790.

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Author details

1. Mayu Yamada

   Graduate School of Engineering Science, Osaka University, Osaka, Japan

   Contribution

   Data curation, Formal analysis, Software, Validation, Visualization, Writing – original draft

   Contributed equally with

   Hirono Ohashi and Shunsuke Shigaki

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9855-9157

2. Hirono Ohashi

   Graduate School of Engineering Science, Osaka University, Osaka, Japan

   Contribution

   Data curation, Formal analysis, Methodology, Visualization, Writing – review and editing

   Contributed equally with

   Mayu Yamada and Shunsuke Shigaki

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5303-7200

3. Koh Hosoda

   Graduate School of Engineering Science, Osaka University, Osaka, Japan

   Contribution

   Investigation, Methodology, Supervision, Writing – review and editing

   Contributed equally with

   Daisuke Kurabayashi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8392-1021

4. Daisuke Kurabayashi

   Department of Systems and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan

   Contribution

   Conceptualization, Funding acquisition, Investigation, Resources, Validation, Visualization, Writing – review and editing

   Contributed equally with

   Koh Hosoda

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3186-6531

5. Shunsuke Shigaki

   Graduate School of Engineering Science, Osaka University, Osaka, Japan

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Mayu Yamada and Hirono Ohashi

   For correspondence

   shigaki@arl.sys.es.osaka-u.ac.jp

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5689-1338

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