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.
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.
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!
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.