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 (email@example.com) with any further questions and discussions!
The Backyard Brains 2018 Summer Research Fellowship is coming to a close, but not before we get some real-world scientific experience in! Our research fellows are nearing the end of their residency at the Backyard Brains lab, and they are about to begin their tenure as neuroscience advocates and Backyard Brains ambassadors. The fellows dropped in on University of Michigan’s Undergraduate Research Opportunity Program (UROP) Symposium during their final week of the fellowship, and each scientist gave a quick poster presentation about the work they’d been doing this summer! The fellows synthesized their data into the time-honored poster format and gave lightning-round pitches of their work to attendees. BYB is in the business of creating citizen scientists, and this real-world application is always a highlight of our fellowship. Check out their posters below!
To foraging and bee-yond: where the bee project is headed next
I can hardly believe it’s time for my last blog post, I still have so many bee puns I haven’t taken advantage of yet! Since my last post, I’ve found that working with bees can be far more dependent on environmental conditions, like amounts of natural nectar available, than anything I do. It’s a plant’s world, and we’re all just living in it. When there was a lot of natural nectar available, I couldn’t get the bees interested in my top-notch, handcrafted, artisan sugar water, try as I might. I thought there might be something wrong with my tunnels that was repelling the bees, or that they might be too close, so I set up feeders in various locations around the hive. Sadly, those too were left untouched.
so many feeders, so little foraging
About a week later, things changed. I started seeing foraging at one of my feeders, and immediately moved a tunnel to that location to take advantage of it. Local beekeeping intelligence confirmed that sources of natural nectar were drying up, making my sugar water suddenly more desirable.
As a result, I finally got some data! I trained the bees to forage at a feeder about halfway down the tunnel, and then removed the feeder and observed where they foraged. My analysis of the video showed that more bees foraged in the target section of the tunnel (the one with the feeder), even when the feeder was removed. This confirmed that the bees had been trained to expect food at a certain point in the tunnel.
average number of bees foraging in each section shows no significant difference between feeder and no feeder
Now that we’ve shown that we can build our own tunnel, bees will forage in it, and that they will remember the location of the feeder, further experiments to test optic flow can be done in the future.
I have had such a fun time getting to share my research and spend quality time with bees! I think I have finally overcome my long-held fear of bees and I really will miss them, my stylish beekeeping outfit, and driving past the medium-sized canada goose sculpture on the way to the hive every day! I may not have been able to test everything I would have liked to this summer, but I was able to show that this experiment has the potential to expand further, and I have learned a lot about making things myself and solving problems (for me, the answer was usually superglue!) Plus, I now feel qualified to be a beekeeper, should anyone trust me with them again.
I have also learned a lot that will help further the experiment next summer, and I look forward to seeing how it develops in the future! For now, it’s time for me to return to return to my other favorite flying insect, Buzz, and our home at Georgia Tech for my last year of undergrad!
frolicking with the bees was my favorite pastime