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.
By Dieu My Nguyen
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.
By Dieu My Nguyen
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.
By Dieu My Nguyen