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>

<ol>
	<li>Russel, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=World+Scientific+Series+in+Robotics+and+Intelligent+Systems.">World Scientific Series in Robotics and Intelligent Systems.</a> Odour detection by mobile robots. , 232 (1999).</li><li>Woodfin, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Trace chemical sensing of explosives. , 496 (2006).</li><li>Wyatt, T. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pheromones and animal behavior. , (2013).</li><li>Weissburg, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+fluid+dynamical+context+of+chemosensory+behavior).">The fluid dynamical context of chemosensory behavior).</a> Biol. Bull. 198, 188-202 (2000).</li><li>Angioy, A. M., Desogus, A., Barbarossa, I. T., Anderson, P., Hansson, B. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extreme+Sensitivity+in+an+Olfactory+System.">Extreme Sensitivity in an Olfactory System.</a> Chem. Senses. 28 (4), 279-284 (2003).</li><li>Kaissling, K. -. E., Guti&#233;rrez, , Marco, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Sensitivity+of+the+insect+nose:+the+example+of+Bombyx+mori.">The Sensitivity of the insect nose: the example of Bombyx mori.</a> Biologically inspired signal processing. , 45-52 (2009).</li><li>Sauer, A. E., Karg, G., Koch, U. T., Dekramer, J. J., Milli, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+portable+EAG+system+for+the+measurement+of+pheromone+concentration+in+the+field.">A portable EAG system for the measurement of pheromone concentration in the field.</a> Chem. Senses. 17 (5), 543-553 (1992).</li><li>Strauch, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=More+than+apples+and+oranges+-+Detecting+cancer+with+a+fruit+fly's+antenna.">More than apples and oranges - Detecting cancer with a fruit fly's antenna.</a> Scientific reports. 4 (3576), 1-9 (2014).</li><li>Marshall, B., Warr, C. G., de Bruyne, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+Volatile+Indicators+of+Illicit+Substances+by+the+Olfactory+Receptors+of+Drosophila+melanogaster.">Detection of Volatile Indicators of Illicit Substances by the Olfactory Receptors of Drosophila melanogaster.</a> Chem. Senses. 35 (7), 613-625 (2010).</li><li>Rains, G. C., Tomberlin, J. K., Kulasiri, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+insect+sniffing+devices+for+detection.">Using insect sniffing devices for detection.</a> Trends Biotechnol. 26 (6), 288-294 (2008).</li><li>King, T. L., Horine, F. M., Daly, K. C., Smith, B. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Explosives+detection+with+hard-wired+moths.">Explosives detection with hard-wired moths.</a> IEEE Trans. Instrumentation and Measurement. 53 (4), 1113-1118 (2004).</li><li>Gardner, J., Bartlett, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Electronic noses. , (1999).</li><li>Martinez, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiphasic+On/Off+pheromone+signaling+in+moths+as+neural+correlates+of+a+search+strategy.">Multiphasic On/Off pheromone signaling in moths as neural correlates of a search strategy.</a> PLoS ONE. 8 (4), (2013).</li><li>Park, K. C., Ochieng, S. A., Zhu, J., Baker, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Odour+discrimination+using+insect+electroantennogram+responses+from+an+insect+antennal+array.">Odour discrimination using insect electroantennogram responses from an insect antennal array.</a> Chem. Senses. 27, 343-352 (2002).</li><li>Spitzer, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bio-Inspired+Nanostructured+Sensor+for+the+Detection+of+Ultralow+Concentrations+of+Explosives.">Bio-Inspired Nanostructured Sensor for the Detection of Ultralow Concentrations of Explosives.</a> Angewandte Chemie. 51 (22), 5334-5338 (2012).</li><li>Furton, K. G., Myers, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+scientific+foundation+and+efficacy+of+the+use+of+canines+as+chemical+detectors+for+explosives.">The scientific foundation and efficacy of the use of canines as chemical detectors for explosives.</a> Talanta. 54, 487-500 (2001).</li><li>Williams, M., Johnston, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Training+and+maintaining+the+performance+of+dogs+(Canis+familiaris)+on+an+increasing+number+of+odor+discriminations+in+a+controlled+setting.">Training and maintaining the performance of dogs (Canis familiaris) on an increasing number of odor discriminations in a controlled setting.</a> Applied Animal Behaviour Science. 78, 55-65 (2002).</li><li>Murlis, J., Elkinton, J. S., Card&#233;, R. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Odour+plumes+and+how+insects+use+them.">Odour plumes and how insects use them.</a> Annu. Rev. Entomol. 37, 505-532 (1992).</li><li>Kaissling, K. E., Lehler, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pheromone-controlled+anemotaxis+in+moths.">Pheromone-controlled anemotaxis in moths.</a> Orientation and communication in Arthropods. , 343-374 (1997).</li><li>Vickers, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+animal+navigation+in+odor+plumes.">Mechanisms of animal navigation in odor plumes.</a> Biol. Bull. 198, 203-212 (2000).</li><li>Martinez, D., Rochel, O., Hugues, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+biomimetic+robot+for+tracking+specific+odors+in+turbulent+plumes.">A biomimetic robot for tracking specific odors in turbulent plumes.</a> Autonomous Robot, Special Issue on Mobile Robot Olfaction. 20, 185-195 (2006).</li><li>Szyszka, P., Stierle, J. S., Biergans, S., Galizia, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Speed+of+Smell:+Odor-Object+Segregation+within+Milliseconds.">The Speed of Smell: Odor-Object Segregation within Milliseconds.</a> PLoS ONE. 7, (2012).</li><li>Bau, J., Justus, K. A., Loudon, C., Card&#233;, R. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electroantennographic+resolution+of+pulsed+pheromone+plumes+in+two+species+of+moths+with+bipectinate+antennae.">Electroantennographic resolution of pulsed pheromone plumes in two species of moths with bipectinate antennae.</a> Chem Senses. 30, 771-780 (2005).</li><li>Barrozo, R. B., Gadenne, C., Anton, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Switching+attraction+to+inhibition:+mating-induced+reversed+role+of+sex+pheromone+in+an+insect.">Switching attraction to inhibition: mating-induced reversed role of sex pheromone in an insect.</a> J. Exp. Biol. 213, 2933-2939 (2010).</li><li>Land, B. R., Wyttenbach, R. A., Johnson, B. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tools+for+physiology+labs:+an+inexpensive+high-performance+amplifier+and+electrode+for+extracellular+recording.">Tools for physiology labs: an inexpensive high-performance amplifier and electrode for extracellular recording.</a> J. Neuroscience Methods. 106, 47-55 (2001).</li><li>Ortiz, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+mobile+electrophysiology+board+for+autonomous+robotics.">A mobile electrophysiology board for autonomous robotics.</a> Master thesis. , (2006).</li><li>Justus, K. A., Card&#233;, R. T., French, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+Properties+of+Antennal+Responses+to+Pheromone+in+Two+Moth+Species.">Dynamic Properties of Antennal Responses to Pheromone in Two Moth Species.</a> J. Neurophysiol. 93, 2233-2239 (2005).</li><li>Martinez, D., Moraud, E. M., Persaud, K. .. C., Marco, S., Gutierrez-Galvez, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reactive+and+cognitive+search+strategies+for+olfactory+robots.">Reactive and cognitive search strategies for olfactory robots.</a> Neuromorphic Olfaction, Frontiers in Neuroengineering Series. , (2013).</li><li>Kuwana, Y., Shimoyama, I., Miura, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Steering+control+of+a+mobile+robot+using+insect+antennae.">Steering control of a mobile robot using insect antennae.</a> Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. , (1995).</li><li>Kuwana, Y., Nagasawa, S., Shimoyama, I., Kanzaki, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthesis+of+the+pheromone-oriented+behaviour+of+silkworm+moths+by+a+mobile+robot+with+moth+antennae+as+pheromone+sensors.">Synthesis of the pheromone-oriented behaviour of silkworm moths by a mobile robot with moth antennae as pheromone sensors.</a> Biosensors and Bioelectronics. 14, 195-202 (1999).</li><li>Myrick, A. J., Park, K. C., Hetling, J. R., Baker, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+and+discrimination+of+mixed+odor+strands+in+overlapping+plumes+using+an+insect-antenna-based+chemosensor+system.">Detection and discrimination of mixed odor strands in overlapping plumes using an insect-antenna-based chemosensor system.</a> J. Chem. Ecol. 35, 118-130 (2009).</li><li>Myrick, A. J., Park, K. C., Hetling, J. R., Baker, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+odor+discrimination+using+a+bioelectronic+sensor+array+based+on+the+insect+electroantennogram.">Real-time odor discrimination using a bioelectronic sensor array based on the insect electroantennogram.</a> Bioinspiration and Biomimetics. 3, (2008).</li><li>Vickers, N. J., Christensen, T. A., Baker, T. C., Hildebrand, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Odour-plume+dynamics+influence+the+brain's+olfactory+code.">Odour-plume dynamics influence the brain's olfactory code.</a> Nature. 410, 466-470 (2001).</li><li>Vergassola, M., Villermaux, E., Shraiman, B. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term='Infotaxis'+as+a+strategy+for+searching+without+gradients.">'Infotaxis' as a strategy for searching without gradients.</a> Nature. 445, 406-409 (2007).</li><li>Martin-Moraud, E., Martinez, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effectiveness+and+robustness+of+robot+infotaxis+for+searching+in+dilute+conditions.">Effectiveness and robustness of robot infotaxis for searching in dilute conditions.</a> Frontiers in neurorobotics. , (2010).</li><li>Masson, J. -. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Olfactory+searches+with+limited+space+perception).">Olfactory searches with limited space perception).</a> PNAS. 110, 11261-11266 (2013).</li><li>Masson, J. -. B., Bailly Bechet, M., Vergassola, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chasing+information+to+search+in+random+environments.">Chasing information to search in random environments.</a> J. Phys. A: Math. Theor. 42, (2009).</li></ol>

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 border="0" cellpadding="0" cellspacing="0" width="681">
	<tbody>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Name of Material/ Equipment</td>
			<td style="width:179px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td style="width:167px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="85" rowspan="2" style="height:85px;width:216px;">Agrotis ipsilon&#160;</td>
			<td style="width:179px;">PISC&#160;</td>
			<td rowspan="2" style="width:119px;"></td>
			<td rowspan="2" style="width:167px;">moth</td>
		</tr>
		<tr height="64">
			<td height="64" style="height:64px;width:179px;">http://www-physiologie-insecte.versailles.inra.fr/indexenglish.php</td>
		</tr>
		<tr height="21">
			<td height="43" rowspan="2" style="height:43px;width:216px;">Robot Khepera III&#160;</td>
			<td style="width:179px;">K-team&#160;</td>
			<td rowspan="2" style="width:119px;"></td>
			<td rowspan="2" style="width:167px;">Khe3Base + KorBotLE + KorWifi</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:179px;">www.k-team.com</td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">KoreIOLE</td>
			<td style="width:179px;">K-team</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">Input/output extension board</td>
		</tr>
		<tr height="21">
			<td height="43" rowspan="2" style="height:43px;width:216px;">EAG-robot interface</td>
			<td style="width:179px;">LORIA&#160;</td>
			<td rowspan="2" style="width:119px;"></td>
			<td rowspan="2" style="width:167px;">Custom-made hardware and software</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:179px;">www.loria.fr</td>
		</tr>
		<tr height="21">
			<td height="42" rowspan="2" style="height:42px;width:216px;">Sirene</td>
			<td rowspan="2" style="width:179px;">LORIA&#160;</td>
			<td rowspan="2" style="width:119px;"></td>
			<td rowspan="2" style="width:167px;">neuronal simulator sirene.gforge.inria.fr</td>
		</tr>
		<tr height="21">
		</tr>
		<tr height="20">
			<td height="41" rowspan="2" style="height:41px;width:216px;">Eagle</td>
			<td rowspan="2" style="width:179px;">CadSoft www.cadsoftusa.com</td>
			<td rowspan="2" style="width:119px;"></td>
			<td rowspan="2" style="width:167px;">PCB design software</td>
		</tr>
		<tr height="21">
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Micromanipulator</td>
			<td style="width:179px;">Narishige / Bio-logic</td>
			<td style="width:119px;">UN-3C</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Magnet base</td>
			<td style="width:179px;">Narishige/ Bio-logic</td>
			<td style="width:119px;">USM-6</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Adapter</td>
			<td style="width:179px;">Narishige/ Bio-logic</td>
			<td style="width:119px;">UX-6-6</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Rotule</td>
			<td style="width:179px;">Narishige/ Bio-logic</td>
			<td style="width:119px;">UPN-B</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Micro scisors</td>
			<td style="width:179px;">MORIA / Phymep</td>
			<td style="width:119px;">15371-92</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Stereo microscope Zeiss St&#233;mi 2000</td>
			<td style="width:179px;">Fisher Scientific</td>
			<td style="width:119px;">B19961</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Light source 20W KL200</td>
			<td style="width:179px;">Fisher Scientific</td>
			<td style="width:119px;">W41745</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Narishige PC-10 Na PC-1</td>
			<td style="width:179px;">Narishige</td>
			<td style="width:119px;">Narishige PC-10</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Capillaries Na PC-1</td>
			<td style="width:179px;">Fisher scientific</td>
			<td style="width:119px;">C01065</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Pheromone cis-7-Dodecenyl acetate(Z7-12:OAc)</td>
			<td style="width:179px;">Sigma-Aldrich</td>
			<td style="width:119px;">259829</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Pack of 3 pipettes&#160;</td>
			<td rowspan="2" style="width:179px;">Eppendorf</td>
			<td rowspan="2" style="width:119px;">4910000514</td>
			<td rowspan="2" style="width:167px;">For pheromone dilution and deposition on paper filter</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">2-20 &#181;l/ 50-200 &#181;l/ 100-1000 &#181;l&#160;</td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Gas sensor TGS2620&#160;</td>
			<td style="width:179px;">Figaro www.figarosensor.com</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">Optional, for comparison with EAG</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">electrode puller&#160;</td>
			<td style="width:179px;">Narishige&#160;</td>
			<td style="width:119px;">PC-10</td>
			<td style="width:167px;"></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/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/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/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/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

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

Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals

Mechanical dissociation of neurons from the central nervous system has the advantage that presynaptic boutons remain attached to the isolated neuron of interest. This allows for examination of synaptic transmission under conditions where the extracellular and postsynaptic intracellular environments can be well controlled. A vibration-based technique without the use of proteases, known as vibrodissociation, is the most popular technique for mechanical isolation. A micropipette, with the tip fire-polished to the shape of a small ball, is placed into a brain slice made from a P1-P21 rodent. The micropipette is vibrated parallel to the slice surface and lowered through the slice thickness resulting in the liberation of isolated neurons. The isolated neurons are ready for study within a few minutes of vibrodissociation. This technique has advantages over the use of primary neuronal cultures, brain slices and enzymatically isolated neurons including: rapid production of viable, relatively mature neurons suitable for electrophysiological and imaging studies; superior control of the extracellular environment free from the influence of neighboring cells; suitability for well-controlled pharmacological experiments using rapid drug application and total cell superfusion; and improved space-clamp in whole-cell recordings relative to neurons in slice or cell culture preparations. This preparation can be used to examine synaptic physiology, pharmacology, modulation and plasticity. Real-time imaging of both pre- and postsynaptic elements in the living cells and boutons is also possible using vibrodissociated neurons. Characterization of the molecular constituents of pre- and postsynaptic elements can also be achieved with immunological and imaging-based approaches.

This report demonstrates a technique for mechanical isolation of individual viable neurons retaining attached presynaptic boutons. Vibrodissociated neurons have the advantages of rapid production, excellent pharmacological control and improved space-clamp without influence from neighboring cells. This method can be used for imaging of synaptic elements and patch-clamp recording.

Mechanical dissociation of neurons from the central nervous system has the advantage that presynaptic boutons remain attached to the isolated neuron of ...

Odorant-induced Responses Recorded from Olfactory Receptor Neurons using the Suction Pipette Technique

Animals sample the odorous environment around them through the chemosensory systems located in the nasal cavity. Chemosensory signals affect complex behaviors such as food choice, predator, conspecific and mate recognition and other socially relevant cues. Olfactory receptor neurons (ORNs) are located in the dorsal part of the nasal cavity embedded in the olfactory epithelium. These bipolar neurons send an axon to the olfactory bulb (see Fig. 1, Reisert &amp; Zhao1, originally published in the Journal of General Physiology) and extend a single dendrite to the epithelial border from where cilia radiate into the mucus that covers the olfactory epithelium. The cilia contain the signal transduction machinery that ultimately leads to excitatory current influx through the ciliary transduction channels, a cyclic nucleotide-gated (CNG) channel and a Ca2+-activated Cl- channel (Fig. 1). The ensuing depolarization triggers action potential generation at the cell body2-4.
In this video we describe the use of the "suction pipette technique" to record odorant-induced responses from ORNs. This method was originally developed to record from rod photoreceptors5 and a variant of this method can be found at jove.com modified to record from mouse cone photoreceptors6. The suction pipette technique was later adapted to also record from ORNs7,8. Briefly, following dissociation of the olfactory epithelium and cell isolation, the entire cell body of an ORN is sucked into the tip of a recording pipette. The dendrite and the cilia remain exposed to the bath solution and thus accessible to solution changes to enable e.g. odorant or pharmacological blocker application. In this configuration, no access to the intracellular environment is gained (no whole-cell voltage clamp) and the intracellular voltage remains free to vary. This allows the simultaneous recording of the slow receptor current that originates at the cilia and fast action potentials fired by the cell body9. The difference in kinetics between these two signals allows them to be separated using different filter settings. This technique can be used on any wild type or knockout mouse or to record selectively from ORNs that also express GFP to label specific subsets of ORNs, e.g. expressing a given odorant receptor or ion channel.

Olfactory receptor neurons (ORNs) convert odor signals first into a receptor current that in turn triggers action potentials that are conveyed to second order neurons in the olfactory bulb. Here we describe the suction pipette technique to record simultaneously the odorant-induced receptor current and action potentials from mouse ORNs.

Animals sample the odorous environment around them through the chemosensory systems located in the nasal cavity. Chemosensory signals affect complex ...

Identification of Olfactory Volatiles using Gas Chromatography-Multi-unit Recordings (GCMR) in the Insect Antennal Lobe

All organisms inhabit a world full of sensory stimuli that determine their behavioral and physiological response to their environment. Olfaction is especially important in insects, which use their olfactory systems to respond to, and discriminate amongst, complex odor stimuli. These odors elicit behaviors that mediate processes such as reproduction and habitat selection1-3. Additionally, chemical sensing by insects mediates behaviors that are highly significant for agriculture and human health, including pollination4-6, herbivory of food crops7, and transmission of disease8,9. Identification of olfactory signals and their role in insect behavior is thus important for understanding both ecological processes and human food resources and well-being.
	To date, the identification of volatiles that drive insect behavior has been difficult and often tedious. Current techniques include gas chromatography-coupled electroantennogram recording (GC-EAG), and gas chromatography-coupled single sensillum recordings (GC-SSR)10-12. These techniques proved to be vital in the identification of bioactive compounds. We have developed a method that uses gas chromatography coupled to multi-channel electrophysiological recordings (termed 'GCMR') from neurons in the antennal lobe (AL; the insect's primary olfactory center)13,14. This state-of-the-art technique allows us to probe how odor information is represented in the insect brain. Moreover, because neural responses to odors at this level of olfactory processing are highly sensitive owing to the degree of convergence of the antenna's receptor neurons into AL neurons, AL recordings will allow the detection of active constituents of natural odors efficiently and with high sensitivity. Here we describe GCMR and give an example of its use.
	Several general steps are involved in the detection of bioactive volatiles and insect response. Volatiles first need to be collected from sources of interest (in this example we use flowers from the genus Mimulus (Phyrmaceae)) and characterized as needed using standard GC-MS techniques14-16. Insects are prepared for study using minimal dissection, after which a recording electrode is inserted into the antennal lobe and multi-channel neural recording begins. Post-processing of the neural data then reveals which particular odorants cause significant neural responses by the insect nervous system.
	Although the example we present here is specific to pollination studies, GCMR can be expanded to a wide range of study organisms and volatile sources. For instance, this method can be used in the identification of odorants attracting or repelling vector insects and crop pests. Moreover, GCMR can also be used to identify attractants for beneficial insects, such as pollinators. The technique may be expanded to non-insect subjects as well.

Identification of Olfactory Volatiles using Gas Chromatography-Multi-unit Recordings GCMR in the Insect Antennal Lobe

Olfactory cues mediate many different behaviors in insects, and are often complex mixtures comprised of tens to hundreds of volatile compounds. Using gas chromatography with multi-channel recording in the insect antennal lobe, we describe a method for the identification of bioactive compounds.

All  organisms inhabit a world full of sensory stimuli that determine their  behavioral and physiological response to their environment. Olfaction is ...

Designing and Implementing Nervous System Simulations on LEGO Robots

We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step of the method is to develop a nervous system simulation of specific reflexive behaviors of an appropriate model organism; here we use the American Lobster. Exteroceptive reflexes mediated by decussating (crossing) neural connections can explain an animal's taxis towards or away from a stimulus as described by Braitenberg and are particularly well suited for investigation using the NXT platform.1 The nervous system simulation is programmed using LabVIEW software on the LEGO Mindstorms platform. Once the nervous system is tuned properly, behavioral experiments are run on the robot and on the animal under identical environmental conditions. By controlling the sensory milieu experienced by the specimens, differences in behavioral outputs can be observed. These differences may point to specific deficiencies in the nervous system model and serve to inform the iteration of the model for the particular behavior under study. This method allows for the experimental manipulation of electronic nervous systems and serves as a way to explore neuroscience hypotheses specifically regarding the neurophysiological basis of simple innate reflexive behaviors. The LEGO Mindstorms NXT kit provides an affordable and efficient platform on which to test preliminary biomimetic robot control schemes. The approach is also well suited for the high school classroom to serve as the foundation for a hands-on inquiry-based biorobotics curriculum.

An approach to neural network modeling on the LEGO Mindstorms robotics platform is presented. The method provides a simulation tool for invertebrate neuroscience research in both the research lab and the classroom. This technique enables the investigation of biomimetic robot control principles.

We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step ...

Whole Mount Immunolabeling of Olfactory Receptor Neurons in the Drosophila Antenna

Odorant molecules bind to their target receptors in a precise and coordinated manner. Each receptor recognizes a specific signal and relays this information to the brain. As such, determining how olfactory information is transferred to the brain, modifying both perception and behavior, merits investigation. Interestingly, there is emerging evidence that cellular transduction and transcriptional factors are involved in the diversification of olfactory receptor neuron. Here we provide a robust whole mount immunological labeling method to assay in vivo olfactory receptor neuron organization. Using this method, we identified all olfactory receptor neurons with anti-ELAV antibody, a known pan-neural marker and Or49a-mCD8::GFP, an olfactory receptor neuron specifically expressed in Nba neuron using anti-GFP antibody.

Whole Mount Immunolabeling of Olfactory Receptor Neurons in the Drosophila Antenna

Herein we describe the process of whole mount immunostaining of Drosophila antennae, which enables us to better understand the molecular mechanisms involved in the diversification of olfactory receptor neurons (ORN)s.

Odorant molecules bind to their target receptors in a precise and coordinated manner. Each receptor recognizes a specific signal and relays this ...

Olfactory Assays for Mouse Models of Neurodegenerative Disease

In many neurodegenerative diseases and particularly in Parkinson&#8217;s disease, deficits in olfaction are reported to occur early in the disease process and may be a useful behavioral marker for early detection. Earlier detection in neurodegenerative disease is a major goal in the field because this is when neuroprotective therapies have the best potential to be effective. Therefore, in preclinical studies testing novel neuroprotective strategies in rodent models of neurodegenerative disease, olfactory assessment could be highly useful in determining therapeutic potential of compounds and translation to the clinic. In the present study we describe a battery of olfactory assays that are useful in measuring olfactory function in mice. The tests presented in this study were chosen because they measure olfaction abilities in mice related to food odors, social odors, and non-social odors. These tests have proven useful in characterizing novel genetic mouse models of Parkinson&#8217;s disease as well as in testing potential disease-modifying therapies.

Impairment in olfactory function is a common feature in many neurodegenerative disorders including Parkinson, Alzheimer, and Huntington diseases. In the present article, we describe a set of tests for assessing olfaction discrimination and detection in mice that can be used to measure olfactory abilities in mouse models of neurodegenerative diseases.

In many neurodegenerative diseases and particularly in Parkinson&#8217;s disease, deficits in olfaction are reported to occur early in the disease process ...

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 ...

Imaging Membrane Potential with Two Types of Genetically Encoded Fluorescent Voltage Sensors

Genetically encoded voltage indicators (GEVIs) have improved to the point where they are beginning to be useful for in vivo recordings. While the ultimate goal is to image neuronal activity in vivo, one must be able to image activity of a single cell to ensure successful in vivo preparations. This procedure will describe how to image membrane potential in a single cell to provide a foundation to eventually image in vivo. Here we describe methods for imaging GEVIs consisting of a voltage-sensing domain fused to either a single fluorescent protein (FP) or two fluorescent proteins capable of F&#246;rster resonance energy transfer (FRET) in vitro. Using an image splitter enables the projection of images created by two different wavelengths onto the same charge-coupled device (CCD) camera simultaneously. The image splitter positions a second filter cube in the light path. This second filter cube consists of a dichroic and two emission filters to separate the donor and acceptor fluorescent wavelengths depending on the FPs of the GEVI. This setup enables the simultaneous recording of both the acceptor and donor fluorescent partners while the membrane potential is manipulated via whole cell patch clamp configuration. When using a GEVI consisting of a single FP, the second filter cube can be removed allowing the mirrors in the image splitter to project a single image onto the CCD camera.

A method for imaging changes in membrane potential using genetically encoded voltage indicators is described.

Genetically encoded voltage indicators (GEVIs) have improved to the point where they are beginning to be useful for in vivo recordings. While the ultimate ...

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 ...

Quadruple Immunostaining of the Olfactory Bulb for Visualization of Olfactory Sensory Axon Molecular Identity Codes

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory Sensory Neuron (OSN) is a bipolar cell with a single dendrite and a single unbranched axon. An OSN expresses only one Olfactory Receptor (OR) gene, OSNs expressing a given type of OR converge their axons to a few sets of invariant glomeruli in the Olfactory Bulb (OB). A remarkable feature of OSN projection is that the expressed ORs play instructive roles in axonal projection. ORs regulate the expression of multiple axon-sorting molecules and generate the combinatorial molecular code of axon-sorting molecules at the OSN axon termini. Thus, to understand the molecular mechanisms of OR-specific axon guidance mechanisms, it is vital to characterize their expression profiles at the OSN axon termini within the same glomerulus. The aim of this article was to introduce methods for collecting as many glomeruli as possible on a single OB section and for performing immunostaining using multiple antibodies. This would allow the comparison and analysis of the expression patterns of axon-sorting molecules without staining variation between OB sections.

Olfactory sensory neurons express a wide variety of axon-sorting molecules to establish proper neural circuitry. This protocol describes an immunohistochemical staining method to visualize combinatorial expressions of axon-sorting molecules at the axon termini of olfactory sensory neurons.

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory ...