This work was funded by the state program Investissements d&#8217;avenir managed by ANR (grant ANR-10-BINF-05 'Pherotaxis').

The protocol is based on a commercially available robot (see Materials table) and male moths (Agrotis ipsilon) with their sex pheromone. Yet, it can be adapted with minor modifications to other insect species, odorants, and robots.
1. Insects
<ol>
	<li>Rear larvae of Agrotis ipsilon Hufnagel (Lepidoptera: Noctuidae) on an artificial diet and maintain them in individual plastic cups until pupation at 23&#177;1 &#176;C and 50&#177;5% relative humidity as described previously24.</li>
	<li>Sex pupae and keep adult males separately from females in plastic boxes. Give them free access to a 20% sucrose solution.</li>
	<li>Perform experiments with males. Male moths are highly sensitive to the sex pheromone emitted by their conspecific females. In A. ipsilon, the major pheromone component, cis-7-dodecenyl acetate (Z7-12:OAc) is the most active compound on the antenna.</li>
</ol>
2. Electrophysiology
<ol>
	<li>Record the EAG from a whole-insect preparation, as described below (Figure 1A). Intact antennae are preferred over excised antennae because they exhibit a longer lifetime (see representative results).</li>
	<li>Chlorinate two silver wires by immersion in concentrated bleach solution for 10-20 min and rinse afterwards. This process prevents electrodes from polarizing. It has to be repeated whenever the baseline drifts during experiments or when the offset voltage between the electrodes becomes too large to be compensated by the amplifier.</li>
	<li>Make glass electrodes from fire polished capillaries with an electrode puller. Fire polishing prevents scratching the chlorinated silver wire with electrodes.</li>
	<li>Anesthetize a male moth with CO2 and place it inside a Styrofoam block with the head protruding from the top.</li>
	<li>Tether the insect&#8217;s head with painter&#8217;s tape around the neck.</li>
	<li>Insert a silver wire serving as reference electrode into the neck.</li>
	<li>Under a stereomicroscope, immobilize one of the antennae with thin strips of painter&#8217;s tape on the tip and the base.</li>
	<li>Cut out the distal 2-3 segments of the antenna with surgical scissors.</li>
	<li>Position the glass electrode near the cut tip of the antenna with a micro-manipulator.</li>
	<li>Cut out the extremity of the glass capillary with forceps to obtain a diameter slightly larger than the cut tip of the antenna.</li>
	<li>Fill the glass pipette with (in mM) 6.4 KCl, 340 glucose, 10 Hepes, 12 MgCl2, 1 CaCl2, 12 NaCl, pH 6.5.</li>
	<li>Insert the cut tip of the antenna into the glass capillary with the micromanipulator.</li>
	<li>Slip the silver wire serving as the recording electrode into the largest extremity of the glass capillary.</li>
</ol>
3. Hardware Interface
<ol>
	<li>Mount the whole preparation, i.e. insect-electrodes-micromanipulator, on a metal plate screwed on the top of the robot (Figure 1B). Connect the electrodes to the robot, as described below.</li>
	<li>Based on previous works25-26, design a hardware interface to adapt the EAG output voltage (order 1 mV at several M&#937;) to the range appropriate for the extension board of the robot. The board accepts 0-5 V analog inputs and a negative voltage below -200 mV may cause severe damage. Follow the steps 3.2.1 to 3.2.4 to design the interface with Eagle.
	<ol>
		<li>Design a &#177;5V power supply from a +12V battery using voltage regulator 78L10 (&#9312; in Figures 2A-2C).</li>
		<li>Design a headstage preamplifier (10X) based on instrumentation amplifier INA121 (&#9314; in Figures 2A-2C).</li>
		<li>Design a second-stage amplifier (25X) with noise filtering (first order high-pass 0.1 Hz filter, second order low-pass 500 Hz filter, notch 50 Hz filter) based on quad op-amps LT1079 (&#9315; in Figures 2A-2C).</li>
		<li>Design a signal conditioning stage that computes with op-amp LT1079 and diode 1N4148 (&#9316; in Figures 2A-2C). The total gain is 250 and the EAG output is in the range 0-5 V with zero being at 2.5 V.</li>
	</ol>
	</li>
	<li>Connect the electrodes to the differential EAG inputs (&#9313; in Figures 2A-2C). Connect the recording electrode to the inverting input of the INA121 to obtain positive EAGs.</li>
	<li>Connect the EAG output (&#9317; in Figures 2A-2C) to the 12 analog inputs of the extension board of the robot. As each input is read sequentially every millisecond, the sampling frequency is 1 KHz.</li>
</ol>
4. Software Interface
The main threads contain a graphical user interface (GUI), methods for signal detection and various functions for controlling the robot.
<ol>
	<li>Write a GUI (Figure 2D) in Qt-C++ for data visualization, digital filtering (20 Hz 5th order Butterworth low pass filter) and odor detection from the EAG. The latter can be performed in two ways: either by deconvolving the EAG with an appropriate filter (engineering approach, section 4.2), or by modeling the neural mechanisms that allow fast and reliable pheromone detection in A. ipsilon moths (bioinspired approach, section 4.3).</li>
	<li>Deconvolution filter. The EAG is well described by a nonlinear cascade27 that consists of a static nonlinearity <img alt="Equation 1" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq1.jpg" /> and a 1st order low-pass filter with exponential impulse function <img alt="Equation 2" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq2.jpg" /> for <img alt="Equation 3" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq3.jpg" />, see Figure 3A. In response to fluctuating odor concentration <img alt="Equation 4" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq4.jpg" />, the EAG output is given by the convolution integral <img alt="Equation 5" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq5.jpg" />. Deconvolution is simply obtained by the inverse of the system; that is <img alt="Equation 6" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq6.jpg" /> in the frequency domain. Then, <img alt="Equation 7" fo:content-width="1.2in" src="/files/ftp_upload/51704/51704eq7.jpg" /> as the Fourier transform of the impulse response is <img alt="Equation 8" fo:content-width="1.2in" src="/files/ftp_upload/51704/51704eq8.jpg" />. For signal detection, follow the steps 4.2.1 to 4.2.3.
	<ol>
		<li>Perform the deconvolution process in the time domain as <img alt="Equation 9" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq9.jpg" /> and <img alt="Equation 10" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq10.jpg" />, Figure 3B.</li>
		<li>Approximate the nonlinearity <img alt="Equation 11" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq11.jpg" /> by a polynomial function. Fit the polynomial parameters and the time constant on input-output data pairs to minimize the mean square error between real <img alt="Equation 4" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq4.jpg" /> and reconstructed<img alt="Equation 12" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq12.jpg" />odor concentration.</li>
		<li>Detect the pheromone hits whenever <img alt="Equation 12" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq12.jpg" /> exceeds a predefined threshold.</li>
	</ol>
	</li>
	<li>Neuromorphic detector. An alternative approach to detection consists in mimicking biology. In A. ipsilon moths, central neurons receiving input from the antenna respond to the pheromone with a stereotypical firing pattern of excitation-inhibition13. A Hodgkin&#8211;Huxley type neuron model with four ionic currents <img alt="Equation 13" fo:content-width="0.3in" src="/files/ftp_upload/51704/51704eq13.jpg" /> (a delayed rectifier K+ current, voltage-gated Na+ and Ca2+ currents, a small conductance Ca2+-dependent K+ current) was previously developed to reproduce the observed physiological responses13. For signal detection, follow the steps 4.3.1 to 4.3.3.
	<ol>
		<li>Implement the neuron model as differential equations. Use the EAG signal as input current <img alt="Equation 14" fo:content-width="1.2in" src="/files/ftp_upload/51704/51704eq14.jpg" /> in the evolution of the membrane potential <img alt="Equation 15" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq15.jpg" />. Use a membrane capacitance C = 22.9 pF and a leak current given by <img alt="Equation 16" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq16.jpg" /> with conductance gL = 0.011161 &#181;S and reversal potential EL = -61.4 mV. The ionic currents are described by <img alt="Equation 17" fo:content-width="1.2in" src="/files/ftp_upload/51704/51704eq17.jpg" /> with <img alt="Equation 18" fo:content-width="2in" src="/files/ftp_upload/51704/51704eq18.jpg" />by where <img alt="Equation 19" fo:content-width="1.2in" src="/files/ftp_upload/51704/51704eq19.jpg" /> are nonlinear functions of V. See previous work13 for the details.</li>
		<li>Simulate the neuron model in real time with Sirene by integrating the differential equations with a 4th order Runge-Kutta method and a time step <img alt="Equation 20" fo:content-width="0.1in" src="/files/ftp_upload/51704/51704eq20.jpg" /> = 0.01 msec. Run the spike test V(tf) &#62;0 mV and V(tf - <img alt="Equation 20" fo:content-width="0.1in" src="/files/ftp_upload/51704/51704eq20.jpg" />) &#60;0 mV online to obtain spike times and interspike intervals.</li>
		<li>Detect the pheromone hits whenever a burst of excitation (3 consecutive interspike intervals &#60;70 msec) is followed by inhibition (interspike interval &#8805;350 msec), see Figure 3C.</li>
	</ol>
	</li>
</ol>

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Authors have nothing to disclose.

Almost twenty years ago, Kanzaki and his colleagues pioneered the idea of using EAGs on olfactory robots29-30. Their technique was originally based on excised antennae. Here, we recorded from intact antennae to improve the sensitivity and the lifetime of the preparation. Other studies31-32 also noticed the superiority of whole-body preparations over isolated antennae. In our robotic experiments, we experienced stable recordings within a day. In contrast, EAGs recorded on isolated antennae have a lifetime of 2 hr (Figure 5).
Our EAG-robotic platform was primarily developed to test biological hypotheses about olfactory coding and search strategies in insects13. Similar to central neurons receiving input from insect antennae, we connected a neuron model to a real moth antenna on a robot and performed pheromone detection based on its firing pattern. Detection and non-detection events were then used to drive the robot toward the source of pheromone. The reactive search strategy considered here was inspired by the behavioral patterns of male moths attracted by a sex pheromone. It performed well in laboratory conditions (Figure 6), allowing the localization of a low emission source (pheromone dose of 10 &#956;g in our case versus 10 mg in previous work24) in a relatively large search space (initial distance from source of 2 m versus 10 cm in previous experiments20-21).
These robotic experiments should be considered as a proof of concept showing that insect antennae are suitable for robotic olfactory searches. Although insect antennae are known to respond to toxic gases, drugs and explosives9-11, several extensions are needed for coping with real world applications. First, a more sophisticated search method34-36 may be more efficient at distances beyond 10&#160;m, when the reacquisition of the plume becomes very unlikely. Second, it may be necessary to combine EAGs from different species in a bio-electronic nose configuration14 in order to detect odorants of interests. Third, stereo sensing capabilities obtained by recording from the two antennae of the same insect may prove beneficial in terms of effectiveness. Two sensors employed in parallel may indeed increase directionality. Fourth, extensions of the search strategy to collective robotic searches37 are ought to be considered for practical applications even if they are not biologically relevant in the case of moths.

Nowadays, animals like dogs are frequently used in safety and security applications that involve the localization of chemical leaks, drugs and explosives because of their excellent smell detection capabilities16. Yet, they show behavioral variations, get tired after extensive work, and require frequent retraining as their performance decreases over time17. One way to circumvent these limitations is to replace trained dogs by olfactory robots.
Nonetheless, tracking scents and odor sources is a major challenge in robotics. In turbulent environments, the landscape of an odor plume is very heterogeneous and unsteady, and consists of sporadically located patches4. Even at moderate distances from the source, as short as few meters, detections become sporadic and only provide cues intermittently. Furthermore, local concentration gradients during detections do not generally point towards the source. Given discontinuous flow of information and limited local information when detections are made how to navigate a robot toward the source?
It is well known that insects such as male moths use chemical communication to successfully locate their mates over long distances (hundreds of meters). To do so, they adopt a stereotypical behavior18-20: they surge upwind upon sensing an odor patch and perform an extended search called casting when odor information vanishes. This surge-casting strategy is purely reactive, i.e. actions are completely determined by current perceptions (detection and non-detection events). Yet, its implementation on olfactory robots had limited success in the past because the detection of odor patches is hampered by the slowness of artificial gas sensors.
Metal-oxide sensors used in most of the olfactory robots have response and recovery times of several tens of seconds so that they generally filter out the concentration fluctuations encountered in turbulent plumes21. In contrast, the response time of insect chemoreceptors is much shorter, e.g., the rise time of insect electroantennograms (EAGs) is less than 50 msec22. Consequently, by using insect EAGs, odor pulses are resolved at frequencies of several Hertz23. This property makes EAG sensors well suited for the detection of odor filaments in natural plumes. We describe here a protocol for embedding insect EAGs on robots allowing for efficient olfactory searches using surge and casting strategies.

<table><tbody><tr><td>&lt;em&gt;Agrotis ipsilon&lt;/em&gt;</td><td>PISC &lt;br /&gt;http://www-physiologie-insecte.versailles.inra.fr/indexenglish.php</td><td></td><td>moth</td></tr><tr><td>Robot Khepera III</td><td>K-team &lt;br /&gt;www.k-team.com</td><td></td><td>Khe3Base + KorBotLE + KorWifi</td></tr><tr><td>KoreIOLE</td><td>K-team</td><td></td><td>Input/output extension board</td></tr><tr><td>EAG-robot interface</td><td>LORIA &lt;br /&gt;www.loria.fr</td><td></td><td>Custom-made hardware and software</td></tr><tr><td>Sirene</td><td>LORIA&amp;nbsp;</td><td></td><td>neuronal simulator sirene.gforge.inria.fr</td></tr><tr><td>Eagle</td><td>CadSoft www.cadsoftusa.com</td><td></td><td>PCB design software</td></tr><tr><td>Micromanipulator</td><td>Narishige / Bio-logic</td><td>UN-3C</td><td></td></tr><tr><td>Magnet base</td><td>Narishige/ Bio-logic</td><td>USM-6</td><td></td></tr><tr><td>Adapter</td><td>Narishige/ Bio-logic</td><td>UX-6-6</td><td></td></tr><tr><td>Rotule</td><td>Narishige/ Bio-logic</td><td>UPN-B</td><td></td></tr><tr><td>Micro scisors</td><td>MORIA / Phymep</td><td>15371-92</td><td></td></tr><tr><td>Stereo microscope Zeiss St&amp;eacute;mi 2000</td><td>Fisher Scientific</td><td>B19961</td><td></td></tr><tr><td>Light source 20 W KL200</td><td>Fisher Scientific</td><td>W41745</td><td></td></tr><tr><td>Narishige PC-10 Na PC-1</td><td>Narishige</td><td>Narishige PC-10</td><td></td></tr><tr><td>Capillaries Na PC-1</td><td>Fisher scientific</td><td>C01065</td><td></td></tr><tr><td>Pheromone cis-7-Dodecenyl acetate(Z7-12:OAc)</td><td>Sigma-Aldrich</td><td>259829</td><td></td></tr><tr><td>Pack of 3 pipettes: 2-20 &amp;micro;l/ 50-200 &amp;micro;l/ 100-1,000 &amp;micro;l</td><td>Eppendorf</td><td>4910000514</td><td>For pheromone dilution and deposition on paper filter</td></tr><tr><td>Gas sensor TGS2620&amp;nbsp;</td><td>Figaro www.figarosensor.com</td><td></td><td>Optional, for comparison with EAG</td></tr><tr><td>Electrode puller</td><td>Narishige&amp;nbsp;</td><td>PC-10</td><td></td></tr></tbody></table>

using insect electroantennogram sensors on autonomous robots for olfactory searches

Robots designed to track chemical leaks in hazardous industrial facilities1 or explosive traces in landmine fields2 face the same problem as insects foraging for food or searching for mates3: the olfactory search is constrained by the physics of turbulent transport4. The concentration landscape of wind borne odors is discontinuous and consists of sporadically located patches. A pre-requisite to olfactory search is that intermittent odor patches are detected. Because of its high speed and sensitivity5-6, the olfactory organ of insects provides a unique opportunity for detection. Insect antennae have been used in the past to detect not only sex pheromones7 but also chemicals that are relevant to humans, e.g., volatile compounds emanating from cancer cells8 or toxic and illicit substances9-11. We describe here a protocol for using insect antennae on autonomous robots and present a proof of concept for tracking odor plumes to their source. The global response of olfactory neurons is recorded in situ in the form of electroantennograms (EAGs). Our experimental design, based on a whole insect preparation, allows stable recordings within a working day. In comparison, EAGs on excised antennae have a lifetime of 2 hr. A custom hardware/software interface was developed between the EAG electrodes and a robot. The measurement system resolves individual odor patches up to 10 Hz, which exceeds the time scale of artificial chemical sensors12. The efficiency of EAG sensors for olfactory searches is further demonstrated in driving the robot toward a source of pheromone. By using identical olfactory stimuli and sensors as in real animals, our robotic platform provides a direct means for testing biological hypotheses about olfactory coding and search strategies13. It may also prove beneficial for detecting other odorants of interests by combining EAGs from different insect species in a bioelectronic nose configuration14 or using nanostructured gas sensors that mimic insect antennae15.

The protocol described above was first tested with short 20 msec pulses of pheromone (dose 1 &#956;g and 10 &#956;g) directly puffed on the antenna. Figure 4A shows the EAGs in response to pheromone pulses. They are positive because the recording electrode was connected to the inverting input of the amplifier, as described in step&#160;3.3. As indicated by the power spectrum, the measurement system is able to resolve pheromone pulses up to 10 Hz. For comparison, we also tested a commercially available gas sensor. The TGS2620 is a metal oxide sensor manufactured for the detection of solvent vapors. Although the sensor presents a high sensitivity to ethanol, it was unable to follow variations in concentration (see the dashed curve in Figure 4B). The problem came from the sensor housing. The TGS2620 is commercialized with a cap that has a flame-proof stainless steel gauze. The response time is slow because, in practice, it takes a certain time for the gas to diffuse through the gauze and reach the metal oxide surface. Recovery is also slow because it takes time to clean the sensor when the gas is trapped inside the cap. We therefore removed the cap and this modification improved the dynamics significantly (see the plain curve in Figure 4B). Still, there was a factor ten between the EAG and the TGS2620 (10 Hz versus 1 Hz). This comparison is nevertheless qualitative as the EAG and the TGS2620 were not tested in the same conditions.
We then assessed the stability over time of our whole-insect preparation (n = 12 moths) as compared to excised antennae (n = 7 antennae). The EAG was recorded periodically in response to pheromone stimulations (duration 500 msec, dose 1 &#956;g). Raw EAGs (in mV) were converted to relative EAGs (percentages of initial value obtained at time t = 0). Figure 5 shows very good stability of our whole-insect preparation within a working day. In contrast, EAGs recorded on isolated antennae decrease rapidly over time so that the signal falls to one half of its initial value after only 1.5 hr. This time dependence is well described by an exponential decay with a lifetime of 2 hr.
Finally, we tested the ability of the EAG robotic plateform to search for an odor source (pheromone compound Z7-12:OAc) using a reactive search strategy (Figure 6A). The search strategy combines upwind surge every time the pheromone is detected with spiral casting in the absence of detections28. The presence of pheromone is detected from the EAG by the neuromorphic detector, as described in step 4.3. Two examples of EAG recorded during the search are shown in Figure 6B. Without the odor source, the EAG remains around zero (i.e. 2.5 V) with very few or no detections. The robot performs spiral casting and generally leaves the search space before reaching the target location (in 92% of the trials, n = 26 trials, Figure 6C right). With the odor source (Figure 6C left), the EAG presents bursts of activity (detections) intertwined with periods of silence (no detections). Spiral casting mainly occurs at the plume contour (Figure 6C left, red line) and appears to be an efficient strategy for relocating the plume centerline when the odor is lost. In this condition, the source is generally found (success rate = 96%, n = 44 trials).
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51704/51704fig1highres.jpg" width="500" /> 
Figure 1.&#160;Whole-insect EAG preparation and robotic setup.&#160;A) The electroantennogram (EAG) is recorded from a whole-insect preparation (see text for details). B) The preparation is mounted on the robot. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/51704/51704fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51704/51704fig2highres.jpg" width="500" /> 
Figure 2.&#160;Hardware-software interface. A) Eagle schematic of the hardware. The circuit consists of six sections (see text for details). It allows filtering (frequency band 0.1-500 Hz, notch at 50 Hz), amplification (total gain 250X) and signal conditioning in the range 0-5 V. B) Eagle layout showing lines of copper (the top is in red and the bottom in blue) and holes (in green). C) Printed circuit board (PCB) showing the discrete elements. D) Graphical user interface (GUI) written in Qt-C++ for data visualization (red trace = EAG input, green trace = neuron model output), filter design and signal detection. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/51704/51704fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51704/51704fig3highres.jpg" width="500" /> 
Figure 3.&#160;Signal detection from the EAG. A) Electroantennogram (EAG) model. The EAG is modeled by a nonlinear cascade27 that consists of a static nonlinearity followed by a 1st order low pass filter with exponential impulse function <img alt="Equation 21" fo:content-width=".2in" src="/files/ftp_upload/51704/51704eq21.jpg" />. The EAG output is given by the convolution integral with <img alt="Equation 22" fo:content-width="2in" src="/files/ftp_upload/51704/51704eq22.jpg" />. B) Engineering approach. The deconvolution filter writes <img alt="Equation 23" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq23.jpg" /> and <img alt="Equation 24" fo:content-width="1in" src="/files/ftp_upload/51704/51704eq24.jpg" />, see text for details. Odor encounters (hits) are detected whenever<img alt="Equation 12" src="/files/ftp_upload/51704/51704eq12.jpg" />exceeds a predefined threshold. C) Bio-inspired approach. A Hodgkin&#8211;Huxley type neuron model with five internal currents (leak, K+, Na+, Ca2+ and SK) is used to reproduce the observed firing pattern of excitation-inhibition (E-I) observed experimentally13. For signal detection, the EAG signal is used as input current and hits are detected whenever a burst of excitation is followed by inhibition in the firing activity.
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51704/51704fig4highres.jpg" width="500" /> 
Figure 4.&#160;EAG response time. A) EAG recordings in response to 20 msec pheromone pulses (dose 1 &#956;g and 10 &#956;g) delivered at different rates (1, 2, 4, 6, 8, and 10 pulses/sec). The normalized EAG power spectrum is shown for a stimulus pulsed at 1 and 10 Hz (dose 1 &#956;g and 10 &#956;g). The EAG resolves individual pulses up to 10 Hz. B) Recordings from gas sensor TGS2620 in response to ethanol (fluctuating concentration). The dashed and plain curves are the sensor response with and without the cap, respectively. The sensor with the cap has a response time of tens of seconds and thus cannot follows the fluctuations in the gas concentration. The TGS2620 without cap resolves individual fluctuations up to 1 Hz. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/51704/51704fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/51704/51704fig5highres.jpg" width="500" /> 
Figure 5.&#160;EAG stability (whole insect preparation vs excised antenna). The EAG was recorded every hour during 8 hr for the whole-insect preparation (n = 12 moths) and every 20 min during 3.2 hr for excised antennae (n = 7 antennae). The figure shows relative EAGs (percentages of initial value obtained at time t = 0). The time dependence for excised antennae is well fitted by an exponential decay with a lifetime of 2 hr (half-life of 1.5 hr).
<img alt="Figure 6" fo:content-width="5in" src="/files/ftp_upload/51704/51704fig6highres.jpg" width="500" /> 
Figure 6.&#160;Robotic experiments.&#160;A) The surge-casting strategy combines upwind surge in the presence of the odor with spiral casting in its absence28. B) Typical EAG recorded during the search while the robot is moving (with and without the odor). C) Robot trajectories with odor (n = 44 trials) and without odor (n = 26 trials). The red dashed line represents the plume contour where 90% of all detections occurred during the trials. Experimental conditions : search space = 4 m x 2.5 m, robot&#8217;s speed = 5.6 cm/sec, target = 10 &#956;g of pheromone deposited on a paper filter and replaced every 2 trials, robot initial location = 2 m from target, wind velocity = 0.9 &#177; 0.2 m/sec at target location. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/51704/51704fig6highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches at JoVE.com

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

We describe a protocol for using insect antennae in the form of electroantennograms (EAGs) on autonomous robots. Our experimental design allows stable recordings within a day and resolves individual odor patches up to 10 Hz. The efficiency of EAG sensors for olfactory searches is demonstrated in driving a robot toward an odor source.

Robots designed to track chemical leaks in hazardous industrial facilities1 or explosive traces in landmine fields2 face the same problem as insects ...

using-insect-electroantennogram-sensors-on-autonomous-robots-for

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

22.9K Views.  Centre National de la Recherche Scientifique (CNRS). We describe a protocol for using insect antennae in the form of electroantennograms (EAGs) on autonomous robots. Our experimental design allows stable recordings within a day and resolves individual odor patches up to 10 Hz. The efficiency of EAG sensors for olfactory searches is demonstrated in driving a robot toward an odor source.

Video: Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Electrophysiological Measurements from a Moth Olfactory System

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively. These approaches have been exploited for getting a better understanding of insect chemical communication. The identified stimuli can then be used as either attractants or repellents in management strategies for insect pests.

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively.

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single ...

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Existing systems to measure insect emergence patterns have limitations; they are only partially automated and are limited in the maximum number of emerging insects they can detect. In order to obtain precise measurement of insect emergence, it is necessary for systems to be semi-automated and able to measure large numbers of emerging insects. We addressed these issues by designing and building a system that is automated and can measure emergence of up to 1200 insects. We modified the existing &#34;falling-ball&#34; system using Arduino microcontrollers to automate data collection and expand the sample size through multiple data channels. Multiple data channels enable the user to not only increase their sample size, but also allows for multiple treatments to be run simultaneously in a single experiment. Furthermore, we created an R script to automatically visualize the data as a bubble plot, while also calculating the median day and time of emergence. The current system was designed using 3D printing so the user can modify the system to be adjusted for different species of insects. The goal of this protocol is to investigate important questions in chronobiology and stress physiology, using this precise and automated system to measure insect emergence patterns.

Measurement of insect emergence patterns requires precision. Existing systems are only semi-automated and sample size is limited. We addressed these issues by designing a system using microcontrollers to precisely measure the time of emergence of large numbers of emerging insects.

Existing systems to measure insect emergence patterns have limitations; they are only partially automated and are limited in the maximum number of ...

Environment

Flying Insect Detection and Classification with Inexpensive Sensors

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect&#8217;s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.

We proposed a system that uses inexpensive, noninvasive pseudo-acoustic optical sensors to automatically and accurately detect, count, and classify flying insects based on their flying sound.

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow ...

Bioengineering

Multi-unit Recording Methods to Characterize Neural Activity in the Locust (Schistocerca Americana) Olfactory Circuits

Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time1. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons)2,3. The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning4,5. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits.
First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs6,7. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode3. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings8. We provide details of our experimental setup and present representative recording traces for each of these techniques.

Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits

We demonstrate variations of the extracellular multi-unit recording technique to characterize odor-evoked responses in the first three stages of the invertebrate olfactory pathway. These techniques can easily be adapted to examine ensemble activity in other neural systems as well.

Detection and interpretation of olfactory cues are critical for the survival  of many organisms. Remarkably, species across phyla have strikingly similar ...

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their applicability in environmental and security monitoring and search-and-rescue operations. Small drones with commercial metal-oxide-semiconductor (MOX) gas sensors have been developed for odor source localization; however, their real-time-odor-detection performance has proven inadequate. However, biosensing technologies based on insect olfactory systems exhibit relatively high sensitivity, selectivity, and real-time response with respect to odorant molecules compared to commercial MOX gas sensors. In such devices, excised insect antennae function as portable odorant biosensor elements and have been found to deliver excellent sensing performance. This study presents experimental protocols for odorant-molecule detection in the air using a small autonomous bio-hybrid drone based on a mountable electroantennography (EAG) device incorporating silkmoth antennae.
We developed a mountable EAG device including sensing/processing parts with a Wi-Fi module. The device was equipped with a simple sensor enclosure to enhance the sensor directivity. Thus, odor source localization was conducted using the spiral-surge algorithm, which does not assume an upwind direction. The experimental bio-hybrid odor-detecting drone identified real-time odorant-concentration differences in a pseudo-open environment (outside a wind tunnel) and localized the source. The developed drone and associated system can serve as an efficient odorant molecule-detection tool and a suitable flight platform for developing odor source localization algorithms owing to its high programmability.

This study introduces experimental protocols for a bio-hybrid odor-detecting drone based on silkmoth antennae. The operation of an experimental electroantennogram device with silkmoth antennae is presented, in addition to the structure of a bio-hybrid drone designed for odor source localization using the spiral-surge algorithm.

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their ...

Engineering

Using Single Sensillum Recording to Detect Olfactory Neuron Responses of Bed Bugs to Semiochemicals

The insect olfactory system plays an important role in detecting semiochemicals in the environment. In particular, the antennal sensilla which house single or multiple neurons inside, are considered to make the major contribution in responding to the chemical stimuli. By directly recording action potential in the olfactory sensillum after exposure to stimuli, single sensillum recording (SSR) technique provides a powerful approach for investigating the neural responses of insects to chemical stimuli. For the bed bug, which is a notorious human parasite, multiple types of olfactory sensillum have been characterized. In this study, we demonstrated neural responses of bed bug olfactory sensilla to two chemical stimuli and the dose-dependent responses to one of them using the SSR method. This approach enables researchers to conduct early screening for individual chemical stimuli on the bed bug olfactory sensilla, which would provide valuable information for the development of new bed bug attractants or repellents and benefits the bed bug control efforts.

Bed bugs rely on olfactory receptor neurons housed in their antennal olfactory sensilla to detect semiochemicals in the environment. Utilizing single sensillum recording, we demonstrate a method to evaluate bed bug response to semiochemicals and explore the coding process involved.

The insect olfactory system plays an important role in detecting semiochemicals in the environment. In particular, the antennal sensilla which house ...

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor ...

Localization of Odorant Receptor Genes in Locust Antennae by RNA In Situ Hybridization

Insects have evolved sophisticated olfactory reception systems to sense exogenous chemical signals. These chemical signals are transduced by Olfactory Receptor Neurons (ORNs) housed in hair-like structures, called chemosensilla, of the antennae. On the ORNs&#39; membranes, Odorant Receptors (ORs) are believed to be involved in odor coding. Thus, being able to identify genes localized to the ORNs is necessary to recognize OR genes, and provides a fundamental basis for further functional in situ studies. The RNA expression levels of specific ORs in insect antennae are very low, and preserving insect tissue for histology is challenging. Thus, it is difficult to localize an OR to a specific type of sensilla using RNA in situ hybridization. In this paper, a detailed and highly effective RNA in situ hybridization protocol particularly for lowly expressed OR genes of insects, is introduced. In addition, a specific OR gene was identified by conducting double-color fluorescent in situ hybridization experiments using a co-expressing receptor gene, Orco, as a marker.

Localization of Odorant Receptor Genes in Locust Antennae by RNA In Situ Hybridization

This paper describes a detailed and highly effective RNA in situ hybridization protocol particularly for low-level expressed Odorant Receptor (OR) genes, as well as other genes, in insect antennae using digoxigenin (DIG)-labeled or biotin-labeled probes.

Insects have evolved sophisticated olfactory reception systems to sense exogenous chemical signals. These chemical signals are transduced by Olfactory ...

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

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

Electroantennographic Bioassay as a Screening Tool for Host Plant Volatiles

Plant volatiles play an important role in plant-insect interactions. Herbivorous insects use plant volatiles, known as kairomones, to locate their host plant.1,2 When a host plant is an important agronomic commodity feeding damage by insect pests can inflict serious economic losses to growers. Accordingly, kairomones can be used as attractants to lure or confuse these insects and, thus, offer an environmentally friendly alternative to pesticides for insect control.3 Unfortunately, plants can emit a vast number volatiles with varying compositions and ratios of emissions dependent upon the phenology of the commodity or the time of day. This makes identification of biologically active components or blends of volatile components an arduous process. To help identify the bioactive components of host plant volatile emissions we employ the laboratory-based screening bioassay electroantennography (EAG). EAG is an effective tool to evaluate and record electrophysiologically the olfactory responses of an insect via their antennal receptors. The EAG screening process can help reduce the number of volatiles tested to identify promising bioactive components. However, EAG bioassays only provide information about activation of receptors. It does not provide information about the type of insect behavior the compound elicits; which could be as an attractant, repellent or other type of behavioral response. Volatiles eliciting a significant response by EAG, relative to an appropriate positive control, are typically taken on to further testing of behavioral responses of the insect pest. The experimental design presented will detail the methodology employed to screen almond-based host plant volatiles4,5 by measurement of the electrophysiological antennal responses of an adult insect pest navel orangeworm (Amyelois transitella) to single components and simple blends of components via EAG bioassay. The method utilizes two excised antennae placed across a "fork" electrode holder. The protocol demonstrated here presents a rapid, high-throughput standardized method for screening volatiles. Each volatile is at a set, constant amount as to standardize the stimulus level and thus allow antennal responses to be indicative of the relative chemoreceptivity. The negative control helps eliminate the electrophysiological response to both residual solvent and mechanical force of the puff. The positive control (in this instance acetophenone) is a single compound that has elicited a consistent response from male and female navel orangeworm (NOW) moth. An additional semiochemical standard that provides consistent response and is used for bioassay studies with the male NOW moth is (Z,Z)-11,13-hexdecadienal, an aldehyde component from the female-produced sex pheromone.6-8

A method to rapidly screen host plant volatiles by measurement of the electrophysiological response of adult navel orangeworm (Amyelois transitella) antennae to single components and blends via electroantennographic analysis is demonstrated.

Plant volatiles play an important role in plant-insect interactions. Herbivorous insects use plant volatiles, known as kairomones, to locate their host ...

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

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Electroantennographic Bioassay as a Screening Tool for Host Plant Volatiles