Neuroscience of Grasshopper Jumps

Why are grasshoppers so hard to catch? Let's explore the visual neurons behind the grasshopper's escape mechanism!

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Why are grasshoppers so hard to catch?!

I aim to study the neuroscience behind this question by replicating past studies on grasshopper vision. Grasshoppers can sense an approaching object and quickly hop away to avoid collision with the object because their visual system includes a type of neurons (descending contralateral movement detector, DCMD) that underlies the animal's visual and motor sensitivity to approaching objects, such as predators. I use Backyard Brains’ open source SpikerBox and the SpikeRecorder iPad app to record and visualize the activity of the DCMDs in the form of electrochemical action potentials, or spikes.

Throughout this project, I aim to bring neuroscience research out of the far, far away university labs and design and perform a low-cost and reproducible project using open source and DIY tools, to explore and learn from the neural basis of the grasshopper's escape mechanism.

Somewhere in Portland, there's a restaurant that serves grasshopper sushi rolls. Is it safe? Is it good? I don’t know!

Because I am a grasshopper researcher this summer in Ann Arbor, Michigan, I have other questions in mind: How do people catch these bugs? If you’ve ever tried to catch one, you know that it is nearly uncatchable when its skeletal muscles get to work as you approach with silent steps, trying to capture it for an afternoon snack.

Catching grasshoppers in Ann Arbor is my exciting challenge this summer. Finding out why they are hard to catch is my neuroscience project.


Why are they hard to catch? Because they can quickly jump away when a person or another insect or object approaches it. How are they able to quickly hop away to escape a potential predator or avoid collision with an object? To address this specific question, I will look into the movement detector neurons in the grasshopper’s brain—the organ that fascinates me.

Just as I can see it with my eyes, the grasshopper can see me if I come to it. Or if I show it scenes from Star Wars when spaceships are flying toward the viewer, the grasshopper can see them too and would hop, hop away. That is what researchers Rind and Simmons found in 1992 in their research on the vision of the locust, or a kind of grasshoppers that form swarms. The grasshopper’s nervous system includes a type of visual neurons, called descending contralateral movement detector (DCMD), that receives visual info from the eyes and sends that info to the legs, and underlies the grasshoppers’ ability to visually detect and react to an approaching object, be it a spider looking for a crunch or an astronomically speedy spaceship.

In human language, the brains of these bugs are capable of serious mathematics. In a paper published in 1995, researcher Hatsopoulos and colleagues came up with an equation that describe how the DCMD neurons sense and respond to approaching and receding objects: velocity, or speed, of the approaching image:

multiplied by an exponential function of size of object’s image on the retina: On a high-level consideration of the computational way the brain of the grasshopper functions, the activity of the DCMD neuron is related to how fast the image is coming toward the eye of the grasshopper and the image size on the eye that changes with decreasing distance between object and the eye. The peak in firing is reached before the collision of the object and the grasshopper, and the bug can leap away using their legs to avoid being hit or eaten.

My goals:

In the world of scientific research, disagreements founded upon experimental evidence and thoughtful arguments give rise to scientific progress. In the two above-cited papers, I see several discrepancies between the two groups of researchers. While Rind and Simmons concluded that there was good correlation between the neuron’s activity and the object's acceleration during the exposure of the grasshopper to approaching objects, Hatsopoulos and colleagues used both their computation and experiment to conclude that the correlation was poor. The two papers generally agree that the DCMD neuron’s responses depend on the size and speed of the object. Keeping these ideas in mind, I will see what results my project will yield and I look forward to contributing to the discussion.

Art by Tanner @ All Hands Active, Ann Arbor, MI

I hope to demonstrate that a fun and educational neuroscience project can be done outside of the far far away university labs! I use Backyard Brains’ Neuron SpikerBox that amplifies and visualizes the activity of the DCMDs in the form of electrochemical action potentials, or spikes. I also have an iPad with the SpikeRecorder app, which provides visual stimuli (growing or receding black dots on a white background) in front of the grasshopper's eye as well as records the DCMD activity.

Hop, hop away. This is how the grasshopper stays alive. This is how it continues to exist and eats plants and destroys our crops. But it...

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SpikerBox.v.1.3c. Build Instructions.pdf

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Micromanipulator build instructions.pdf

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micromanipulator 3D file.png

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Electrode build instructions.pdf

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  • 1 × TOTAL COST RANGES FROM $400 TO 900 Depending on how DIY you go!
  • 1 × Grasshoppers FREE - As many as you can catch!
  • 1 × iPad ~$400 or if already have - As screen for visual stimuli and recording and visualizing spikes, in a cumulative SpikeRecorder app
  • 1 × Microscope $200 or ask around or use a phone app! - 20X magnification is sufficient; For surgery on grasshopper and electrode placement
  • 1 × Magnetic stirrer $100 or be creative! -To heat, melt and mix the wax and rosin mixture

View all 12 components

  • Video!

    Dieu My Nguyen09/24/2016 at 22:09 0 comments

  • How does screen brightness affect DCMD response?

    Dieu My Nguyen08/25/2016 at 01:30 0 comments

    Now that I've collected ample data for the "classic" experiment of testing the DCMD response to objects approaching at various sizes and velocities, I want to keep exploring grasshopper vision. So far, the iPad screen is kept at maximum brightness, so the contrast between the white background and the black ball is high and clear. Now, can grasshoppers still see the black ball if the screen brightness is at its darkness? Let's find out!

    Grasshopper G26-072516 is the subject for this test. I performed two extreme brightness levels: the highest and lowest, each for 20 trials with 6cm balls approaching at -2m/s. Note that I measured and changed the amount of brightness by adjusting that brightness bar built into the iPad. So the "lowest" brightness is not complete, pure darkness. The black ball is still identifiable, just very low contrast with the gray background.

    And... I obtained results I did not expect! At max brightness, DCMD firing rate peaks at 95Hz. At minimum brightness, it peaks at 90Hz. Very, very similar firing frequency and peaking profile.

    Does this mean that grasshoppers can see in the dark?! At least I can say with these negative results that grasshoppers might be able to detect approaching objects even if they din't highly contrast with the background.

  • Classic experiments: DCMD response to approaching balls

    Dieu My Nguyen08/25/2016 at 01:09 0 comments

    With the ideal ITI determined, I can move on to the set of core experiments: testing to see how the DCMD neuron behaves when simulated black balls of different sizes and velocities approach the grasshopper's exposed eye. So my little friends spend about 2 hours on top of the SpikerBox for these experiments.

    I continue to process the data in MatLab for better visualization. Here are the results for balls approaching from a constant initial distant of 10cm, 6cm in size, and with various velocities (-2, -4, -6, -8m/s).

    Perievent histogram: showing DCMD firing frequency 2s before and 2s after the simulated collision between the eye and the object:

    Raster plot: showing DCMD spiking pattern across each pair of S and v over time. DCMD firing peaks around collision for objects approaching at -2m/s, and after collision for objects approaching faster:

  • New & improved ITI test

    Dieu My Nguyen08/25/2016 at 00:53 0 comments

    In the 'Preliminary data' log, I had begun my data collection and analysis journey. I first performed the intertrial interval, or ITI, test, to determine the ideal time between 2 stimuli so that the time is long enough to avoid the grasshoppers' habituation to the simulated balls. The results figures I showed in that previous log showed that the 45s ITI was better than the other ITIs in giving us a nice profile of the DCMD neuron activity over time. However, of course, the data visualization could be much improved, and I have been doing that by importing the recordings (stored in JSON files by the SpikeRecorder app) into MatLab (using JSONlab). MatLab yields cleaner and to-scale figures that give us an even better idea of the DCMD profiles in different ITIs.

    Here, compare! These are the old figures, not to scale and all are the same height. So I had to label them all with their frequencies:

    I performed a new ITI experiment on a new grasshopper, G25-072416-01. This time, I used 3 different ITIs that I think are sufficient: 45s, 22.5s, and 1s. All other experimental parameters are kept constant: iPad screen is 0.10m from the grasshopper's eye, balls of 0.06m radius approach at -2m/s (negative for the increasingly shortened distance between the eye and the object). 30 trials per ITI test. And the data is processed in MatLab, and it looks beautiful!

    Sorry the axis labels are too small to read. Horizontal axis: time to collision, from -2 to 2 seconds. Vertical axis: Firing frequency in Hz. Firing frequency is much higher in the 45s ITI, making it a "good" ITI to use for the subsequent experiments.

  • Recording live neurons: the SpikeRecorder app

    Dieu My Nguyen08/24/2016 at 23:31 0 comments

    In the project instructions, I've briefly talked about the BYB SpikeRecorder app that I've been using on an iPad to add to my grasshopper vision project the flavor of a low-cost-and-DIY-albeit-of-great-quality tool. Here, I'll talk about it in a bit more details to give the spotlight to one of the main components of my project.

    Firstly, the purpose of the original SpikeRecorder version that BYB has published is to record data directly to your PC (or tablets & smartphones) while you can observe the recording in real time. There's also the functionality of saving the recording to be played back anytime. And if you're familiar with the classic model of an action potential (aka spikes!), the SpikeRecorder also allows a threshold view, where you can set your threshold and get a snapshot of your spikes.

    This is a classic "spike" event when the electrochemical properties of a neuron is at work. These spikes are essentially changes in voltage due to the chemical and electrical difference inside and outside of a neuron's membrane. Movements of sodium and potassium across the membrane via channels and the way their charges get distributed -- these are the main components of a spike.

    Art by Backyard Brains

    If you're interested in checking out this app and perhaps get some spikes, the app is available for android and ios. And of course, the code is on github for the open source spirit!

    One of my mentors, Stanislav Mircic, is the computer science god of BYB. He graciously added the "Grasshopper experiment" functionality to the app. The app now can provide both the visual stimuli (simulated balls thrown at grasshopper's eye) and recording/analysis of the DCMD neuron activity.

    Sorting a bunch of spikes at once:

    Zooming into one DCMD spike!

  • A new naming system for database!

    Dieu My Nguyen08/24/2016 at 23:12 0 comments

    As I experiment on more and more little grasshoppers, I realize the importance of organization skills. Specifically, I'm talking about how messy my housekeeping of the recordings and analyses have been. In an earlier post, I wrote that my naming system for each grasshopper is in the following format: [day][month][letter indicating order in the day]. While a name of 2408A isn't terrible, what my mentor Greg Gage came up with in a minute is significantly better. (And sitting down with him to discuss my preliminary results also jumpstarted the task of organizing folders and files and sharing in Dropbox.)

    So, now each grasshopper has the following name format: G[number]-[month][day][year]-[test number]. So, G08-070816-01 denotes that the folder containing recordings belonging to the 8th grasshopper I've tested on, on the 8th of July in 2016, for the first test. A second or third test could follow, and new folders are made to keep the data for those tests. So my database is now much more organized:

    While this log is not about building or experimenting or data, it's about a skill that anyone, especially scientists, should have. I can imagine all sorts of problems if all my recorded m4a files stayed in the chaos from before: wrong data analyzed, data from different grasshoppers get mixed up, etc. Good thing I sorted this out before entering the point of no return.

  • Preliminary data

    Dieu My Nguyen07/11/2016 at 02:56 0 comments

    After trials and errors, the electrophysiology setup is ready to collect usable data. I have updated the current version of the protocol and setup on the instructions tab.

    For housekeeping, I give each grasshopper that participates in the experiments a name in the format of [day][month][letter indicating order]. For example, a grasshopper whose DCMDs are recorded today would be named 0710A. If I request the help of another grasshopper, that subject would be 0710B.

    The first series of experiments I am performing aims to record and analyze the activity of the DCMD neuron when the black ball is approaching the grasshopper's eye. My hypothesis is that the neuron's peak firing/activity rate would be around the time when the simulated ball would hypothetically collide with the eye.

    Before I performed the first of these experiments, I needed to determine the "ideal" intertrial interval, ITI, when no visual stimulus is present. This interval between two stimuli is necessary due to the possibility of habituation to the stimuli. If the ITI is too short (e.g. 1 second), the DCMD neuron might no longer consistently fire. I need to quantify the neuronal responses to different intervals and identify an ITI that will be long enough for the neuron to respond to most or all of the approaching balls. The ITIs I chose are: 1, 15, 30, 45, and 60 seconds. All other experimental parameters are kept constant across all ITI tests: with the iPad screen 0.10m from the grasshopper's eye, balls of 0.06m radius approach at -2m/s (negative for the increasingly shortened distance between the eye and the object). 30 trials per ITI test. Here are the results!

    • Horizontal axis: 0 (red dash line) is the time of impact/collision between the eye and the ball. The graphs show the DCMD activity 2 seconds before and after the impact.
    • Vertical axis: Two representations of the clusters of the DCMD spikes, which mostly are around the time of impact. Other "spikes" more than 0.5sec from 0 either direction are most likely noise, a common trouble for electrophysiology recordings, or other strange neuronal activity.

    As you can see, the DCMD firing rates for the ITI of 1sec and 15sec are low and less consistent compared to the rest of the ITIs. The 45sec ITI yields relatively the best profile of the DCMD activity, and so I will use this ITI for my first series of experiments.

    In this series of experiments, these are the constants across all trials:

    • Distance from the grasshopper's eye to the iPad screen: 0.10m
    • Intertrial interval: 45sec
    • Object size: 0.06m
    • Trials each pair of object size and velocity: 16
    • Total experiment time: 60min
    • The approach velocities are varied: -2, -4, -6, -8, -10m/s. Each ball is a combination of the object size and one of the velocities.

    Preliminary results:

    As expected, DMCD peak activity clusters around the time of impact, at 0sec. Interestingly, this peak is about 90msec after the supposed time of impact. Some questions I must ask are: Does this result make intuitive sense, when the neuron supposedly acts as a warning and escape mechanism and its activity should hypothetically peak before the collision so the animal would jump away to avoid being hit? Is the iPad screen big enough and close enough for the grasshopper to really "sense" the danger of collision? What are the possible factors in the iPad app that might yield this result? Is the simulated time of collision (when the ball of a particular size stops expanding) accurately computed and depicted on this graph? I will continue to investigate this and make appropriate adjustments.

  • Experimental Setup & Data Collection Begins!

    Dieu My Nguyen07/06/2016 at 01:39 0 comments

    Materials: check. Grasshoppers: Check. Protocol: Check, and please do check the instructions on the main project profile for the protocol of this experiment. Next step: Setting up the experiment and take off!

    This is how the grasshopper spends an hour of its time for science:

    The iPad screen is placed on the side contralateral (opposite) to where I place the electrode on the grasshopper's neck. Here, the electrode is on the grasshopper's right side, so the iPad is on the left. In the blurry background, you might see a white RadioShack mini speaker, which amplifies the signal sounds and helps me identify neural spikes the DCMD neurons generate during the experiment. Electrophysiology is half seeing the spikes on the oscilloscope (or in this case, the iPad app that can do it all) and hearing them. And the spikes are distinctive! Here, see and listen to my initial test of the setup. In this test, the ball's radius is 6cm (0.06m) approaching at 3m/s.

    Do you hear it? As the black ball gets closer and closer, at a certain size of the ball, there's a swoosh or krrrrr (or whatever you heard) sound that stands out from the base noise of the recordings. That's the DMCD spiking! In the screenshot of the recordings above, the spikes standing tall and distinctive are marked by the red dots. In the field, the grasshopper would probably jump away when the neurons fire, to avoid colliding with whatever object that's coming toward it. In my Backyard Brains lab, by cooperating and responding to the simulated ball, the grasshopper is greatly contributing to vision neuroscience at large and my experience with insect electrophysiology and neuroscience in particular. So, thank you, little grasshopper.

    After I tested the setup and heard those exciting initial spikes, I added a few more finishing details to the setup. As of now, the grasshopper will get a room of its own whenever the experiment is conducted. The lights will be off, so there is sufficient intensity of contrast of the black ball against a white background of the iPad. (Perhaps as followup questions and studies, I can test whether grasshoppers can identify approaching objects in a cluttered background, or what colors can these bugs see.) Noise from other electronic equipment and devices will need to be minimized and thus those devices will be turned off, for optimal signal:noise ratio.

    Thus, the experiment is conducted in darkness like this:

    I will collect data from now on. Stay tuned for an update on the data!

  • Catching grasshoppers!

    Dieu My Nguyen06/29/2016 at 02:44 0 comments

    I went out to the field in Ann Arbor, MI yesterday and in my mind, I wanted to catch at least 20 grasshoppers to last me about two weeks of data collection. After two hours of navigating through a vast tall grass field in the Nichols Arboretum in the scorching summer heat, I had to lower my expectation, to maybe about 1 little grasshopper in my cage and I'd happily go home.

    The grasshopper's abilities to camouflage and escape are responsible for my wasted time. But I shouldn't think of it as wasted time! After all, I am studying one of the reasons why these bugs can escape dangerous predators, like myself who will make the grasshoppers watch circles expanding and contracting on the iPad screen.

    Today, with practice and learning from the WikiHow article on how to catch grasshoppers, I became a capable predator. With one hand, I now can simply come from behind a grasshopper sitting on a leaf and enclose my hand with both leaf and grasshopper wiggling inside. They don't only struggle to escape from my hand, they also vomit a tobacco-colored bile as part of their escape mechanism after being caught. (So many escape methods! I study the mechanism they use to avoid being caught in the first place.) A paper studied this regurgitation behavior as the self-defense of the grasshoppers and found that the vomit can make lizards reject the grasshopper before complete ingestion. But the vomit might not protect the insect from vile humans, at least not when the human is me, determined to catch my study organisms despite the discolorations of my hands.

    For a successful catch in a tall grass field, sweep through the field despite the itch and the thorns. Use both the central and peripheral visual fields. Spot the grasshoppers who blend in too well with the plants and ground (they come in many shapes and colors--mostly green and brown). And be decisive! Grab and pluck that leaf that the alien-eyed bug is sitting on. And ta-da! Happiness.

  • Designing Experimental Setup & Gathering Materials

    Dieu My Nguyen06/25/2016 at 18:50 0 comments

    Below is the preliminary design for my electrophysiology setup. The Backyard Brains SpikerBox has a piece of cardboard on top, so after being anesthetized in ice, the little grasshopper will chill out there for the experiment, which would last about an hour. The grasshopper's belly (ventral side) would face up, so I can place the recording electrode in its neck (where the activity of the DCMD neurons can be picked up) and the reference in either its abdomen or thorax, where I assume the DCMD activity would not be found. Because the DCMDs are theoretically responsive to "contralateral" stimuli that is on the opposite eye, if I place the electrode (using the Backyard Brains 3D printed micromanipulator for precision) on the right side of the grasshopper's neck, I would expose its left eye to the iPad. I will use a level ruler to make sure the angle between the center of the screen (also the center of the stimulus) and the center of the grasshopper's eye is as minimal as possible.

    Art by Tanner @ All Hands Active, MI

    During the trials, the iPad screen will throw a black ball (in a white background for stark contrast) at the grasshopper's eye. Some parameters I can control are: distance between the ball/screen and the eye, the size of the ball, and the ball's velocity of approach (ball expanding in size). What will happen when a small ball is approaching the eye slowly? When it is fast? When the ball is big? We'll see!

    The profile page of this project includes the full list of the materials I need and the detailed instructions, from preparing the little grasshoppers for the experiments to throwing simulated balls at them to recording their DCMD activity.

View all 11 project logs

  • 1
    Step 1

    Build the SpikerBox, micromanipulator, and electrodes. Build instructions for these items are in the files section! Gather the materials:

  • 2
    Step 2

    Anesthetize the grasshopper by placing it in a plastic container (also its home in the lab) and keeping in the fridge (not freezer) for 15-20 minutes or until it is inactive. This keeps the animal still and painless (if insects indeed feel pain) during the upcoming surgery. (Dragonflies are also our faves.)

  • 3
    Step 3

    After anesthesia, tape the grasshopper belly up on the cork board piece on the BYB SpikerBox. Tape all the legs and the abdomen. Leave the head and a little of the thorax exposed—these areas are where electrodes will be placed. (I find masking/painter’s tape to be the easiest to work with.)

View all 11 instructions

Enjoy this project?



Rodrigo Loza wrote 08/25/2016 at 05:16 point

I am sorry my comment is not going to be about neuroscience or anything related to technology, nonetheless i like your project. The point is, i feel sad for the grasshopers ...

  Are you sure? yes | no

Dieu My Nguyen wrote 08/25/2016 at 18:14 point

Hi Rodigo, thank you for your comment. I understand your concern for the grasshoppers. And I completely acknowledge that my project is potentially controversial regarding the ethics of insect usage for research. I hope you acknowledge that my project's main purpose isn't "fun," but education and research to make a particular observation of the natural world. I invite you to check out this Wikipedia page on "pain in invertebrates": It provides a thorough and important discussion on the contentious issue of whether invertebrates, such as grasshoppers, feel something we humans would characterize as "pain," and consequently, whether it is "ethical" to use them for our benefits, in the forms of expanding our knowledge of the world or finding medical treatments for humans. 

And if you'd like to read more on the issue, here is an academic discussion, dated 1989 but relevant: As you can see, such an ethics discourse is quite philosophical and contentious. "Pain," "sentience," and "consciousness" are very abstract and subjective terms which we readily apply to ourselves, but we cannot be certain that it is appropriate to apply them to insects as well. 

That said, however, I certainly do think that practicing science must be done without human arrogance and with appreciation for the natural world. There is a possibility that insects do feel pain and discomfort, and to ensure that I don't incidentally hurt the grasshoppers, I try to minimize the pain by anesthetizing them in ice/the fridge before the surgery. This is similar to the anesthetics used in human surgery. And I try to be gentle during the surgery to not create holes (where electrodes are placed) bigger than necessary and release these subjects back to the fields after the experiment is over. Their life spans are short by nature, but you'd be surprised that lots of them still thrive after the experiments and go back to their grasshopper lives.

Lastly, I want to also post this link to an ethical statement by Backyard Brains, the company I intern for:

My apologies for a long reply. I hope it sufficiently expressed my thoughts on the matter. Thanks so much for your time and consideration. 

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Rodrigo Loza wrote 08/25/2016 at 21:07 point

Actually, i like your arguments. Great project!

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RoGeorge wrote 08/17/2016 at 05:44 point

About a decade ago there were some studies about 'Locust Collision Avoidance' published. Because I intended to replicate the studies, I saved some documents available online, including a PhD dissertation. In the meantime Volvo started a project to implement an Automated Brake System based on the locust research, and I think there are some patents based on this (not sure) because after a year or so, I couldn't find anymore the same online research papers that I have already saved. The nice part was that there were some code implementations for ARM. In case you think you want to look at those papers, just let me know.

Good luck with your project!

  Are you sure? yes | no

Dieu My Nguyen wrote 08/18/2016 at 16:53 point

Hi there, it's so cool to see basic research being applied to the human world! Did Volvo ever come out with a prototype? I can't find much info on the web about it, except that they were inspired by these little bugs and took notes to begin implementing locust-inspired technology. If you have locust papers, please do share. And I would also love to see your replication of the studies!

  Are you sure? yes | no

RoGeorge wrote 08/18/2016 at 19:11 point

I think they come up with a prototype, it's the one I was talking about in the comments of In the meantime they fix it, but I don't know if Volvo is currently selling this feature or not.

The research papers I have are all saved from the Internet, many years ago. I will send you a private message with the download link. It's a 60 MB zip. Please let me know when you finished the download.

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Peter McCloud wrote 06/26/2016 at 03:40 point

The neuroscience behind the grasshopper is really intriguing and I think you've done a good job with the documentation so far. Your work got me thinking about how the mechanisms you've described could potentially be applied to a collision avoidance scheme for drones. It'd have additional complexity because the avoidance scheme would have to work in three dimensional space multiple directions and have an additional mechanism to avoid simply flying into the ground. Anyways, I can see how this work could lead to some really cool applications. 

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Dieu My Nguyen wrote 06/28/2016 at 02:25 point

Peter, thanks for your comment! My project profile still needs many revisions and additional details, but I am glad you find this project interesting. The neuroscientists (Claire Rind and Peter Simmons) whose 1992 paper's study I am replicating have been applying the findings of their basic research on locust collision avoidance mechanism to the motion control of mobile robots. I'm with you-- the additional complexity will be present, and insect science in general can lead to cool bio-inspired designs. Take care!

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