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[Summer’16 Internship] Neuroscience of Grasshopper Jumps: New & improved ITI test

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


[Summer’16 Internship] Neuroscience of Grasshopper Jumps: Recording live neurons: the SpikeRecorder app

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!

By Dieu My Nguyen

[Summer’16 Internship] Neuroscience of Grasshopper Jumps: A new naming system for database!

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