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The End of My Summer of Jellyfish (Or is it?)

Hello all! The summer fellowship is officially over, but it’s not quite the end of the line for the jellies and me! In this final(?) update to my blog series I’ll be recalling the findings I’ve made over this summer, showcasing the poster I presented at the UROP Symposium, sharing my road trip back home (with the jellies in tow!!!), and planning my post-fellowship jellyfish-based research!

Final Fellowship Findings:

I’ve learned a lot about clytia hemisphaerica over this fellowship. This ranges from their appearance and life stages (polyp, ephyrae, medusa) to their husbandry and maintenance needs (acceptable salt levels, daily and weekly water changes, feeding requirements). This newfound knowledge also includes their behaviors and abilities, like how they catch and eat prey or how they dart, zig-zag, and make circles in the water. I have collected a decent number of videos for my jellyfish dataset, and I’ve done some basic position tracking on most of that dataset, but unfortunately the fellowship was over before any rigorous analysis could be completed.

However, this is not the end! I will be dedicating time over the next few weeks to progressing my research by adding features to my jellyfish tracking/analysis software to get more usable stats on the videos, by analyzing jelly video stats using unsupervised machine learning for labeling behaviors, and by getting more raw footage of these wonderful jellies to add to the dataset! (But more on that later.)

Poster Presentation:

This first photo is of the poster I made for and presented at the UROP symposium. It gives a brief introduction on clytia hemisphaerica, explains how I created my dataset (video recordings), and shows what observations and findings were made.

This next photo shows the poster and me in action at the symposium!

I got to meet a lot of exciting people and shared endless amounts of unusual jellyfish facts with them. [Example fun fact: Did you know it’s been confirmed that some jellyfish (like the upside down jellyfish) sleep? This finding by researchers at Caltech (http://www.sciencemag.org/news/2017/09/you-don-t-need-brain-sleep-just-ask-jellyfish) was surprising since jellyfish don’t have brains or even a central nervous system, so sleep must be a more universal activity than previously thought.]

Jelly Road Trip:

The day came much too quickly – the day I had to leave Ann Arbor and go back home to Cincinnati. I spent 7 hours straight packing and loading the car with all the things I’d brought with me or accumulated during my stay.

There was a lot of stuff and it took up a lot of (hard to find) space in my compact-size sedan, but one spot remained clear: the passenger seat.

The passenger seat was reserved for the 2 remaining jellies! I got approval to take them home with me and continue my work in Cincinnati! After 220 miles of highway roads, the jellies finally got to see their new home (and I got to improvise a new DIY tank setup).

Now that the jellies are here, we can start on the post-fellowship jellyfish-based research plans!

Future Work:

Over the next few weeks, I plan to make more recordings of the jellyfish in a wider variety of situations. I’ll try changing environmental variables like lighting, current direction/intensity, salinity, and water temperature.

Some of the features I plan to add to the tracking/analysis software include optical flow options (to track the water current based on the dust particles visible in the videos), ellipse fitting options (to gauge when the jellyfish is actively pulsing), and multiple jelly support (for tracking 2 or more jellyfish at once).

Finally, the machine learning portion of this project will revolve around mostly unsupervised methods in the hopes that behaviors can be found with minimal bias and human error. Some options that were discussed include basic k means clustering as a start followed by other methods like compressing the layers of the neural network to force the algorithm to find patterns that effectively store the original data without losing any information.

This fellowship was a great experience and I’m very excited and grateful for the opportunity to bring my project home with me and continue my research.


Summer Summary

I didn’t write a lot of blog posts this summer because I didn’t have my own research project, but the other research projects kept me plenty busy. I converted over an old BYB library written in a pricy programming language called Matlab into a free open source language called Python. I also cleaned it up and commented out all of the code while I was at it.

I had the privilege of helping out the other fellows with their projects. I got to be a test subject for a couple studies and helped build a bee tunnel. Plus I wrote some code for graphing and analyzing the EAG of the moth experiment as well as some odd functions here and there. Not bad for a recent high school graduate!

I can’t believe it’s all over now. It was a wonderful way to spend a summer. Thank you to everyone who made it possible, especially Greg Gage, Sanja Gage, Etienne Serbe, and Stanislav Mircic. A special thanks to all of the 2018 fellows!


Movement Mind Reader: Hopefully not the end…

I write this on the last day of the fellowship. With a really heavy heart. Eleven weeks went past really fast. Although I shall be back again in Ann Arbor for school in September, it won’t be the same. This was one of the best summers I’ve ever had. I will surely miss everyone at Backyard Brains!

In my last post, I mentioned about how I could perform post-hoc classification to determine whether a person is thinking about movement or not. For most of my time after that, I was working on having a better classification accuracy, by tweaking parameters here and there, collecting more data and validating the results. The average classification rate I achieved was approximately 88%. Which is very good. But, post-hoc classification has no use with respect to application. And so, I have started working on reading continuous data and classifying with a real time interface. But time decided to just fly as fast as it could. So I will definitely continue working on it through the next month. No other major updates about the project for today.

Meanwhile, we were all also preparing for the our poster presentation which was on 1st August. It was my first ever poster presentation, and it turned out to be so motivating and inspiring: looking at the amazing research by so many other students and getting feedback on our research, getting a chance to have a meaningful discussion about our work, all of it was so fruitful and fun.

One more thing which I realised is that I never really discussed why the behaviour of mu rhythms is the way that it is. In the sense, what is the reason why these particular waves disappear with movement or the thought of movement. This is something which should’ve been in the very first blog post, but I guess better late than never? So, there isn’t really a concrete explanation for the behaviour of mu rhythms, but of all the different theories, I came across one which personally to me made the most sense. Feel free to correct me if you feel so! As mentioned before, mu rhythms are most prominent when a person is physically at rest, to be specific when the neurons in sensorimotor region are ‘idling’. However, with the thought of movement or with actual movement, these neurons all start sharing a huge amount of information at the same time. Hence, a very high ‘information capacity’ results into a weak signal. This is similar to the stadium analogy that Greg often uses. When outside the stadium, we can never really figure out what’s going on inside because there are thousands of different voices at the same time. And thus we can never really know what is being said. On the other hand, when everyone is singing the national anthem, we can hear it outside because everyone is saying the exact same thing. Thus it makes sense that the mu rhythms are stronger when all the neurons are in the exact same ‘idling’ state, and they get suppressed with the onset of movement or movement visualisation because they are all firing at the same time and sharing a ton of information. Here’s an image to visualise all that I wrote:

    

Again, this explanation might not be the correct one, it just made sense to me personally.

And with this I conclude. I hope to be able to write again for all of you with further advancements in my project. I would like to thank Greg and everyone else at Backyard Brains for this amazing summer! Feel free to reach out to me  (anusha.joshi@backyardbrains.com) with any further questions and discussions!