Screen start
Hi guys,
How are you? Quick update: I did manage to get the speed reinforcement to work. Thus, I will start the screen this week!
Update
This is the same experiment as I previously showed of Gr66a>Chrimson (ATR). The only difference is that the light was on for the whole experiment, so that the flies could see the light before the entered the arm. Previously the light switched on once the fly went into the arm. The phenotype is much stronger (there is some classical component in it). I was trying to reinforce left or right turns but it does not seem to work after a bit trying out. It makes sense ecologically I think, that the right or left turns are not coupled to the reinforcement systems. I also have been thinking about the CS-US relation bitter taste-turn directions does not make sense ecologically, but maybe if instead of bitter, I apply pain or heat …it could work. I was thinking of reinforcing orientation as well as a speed threshold, or any other variants. What do you think? I would appreciate some ideas. Since I want to make sure about what am I measuring: operant/place/classical…
My update from de Bivorts lab
Schematic of the Y-mazes closed loop. A setup consisting of a rig with many Y-mazes is illuminated from below with a diffuser in between. Above a camera records from the behaving flies in the Y-mazes and track different parameters online over the time course of the experiment with a custom software. The initial paradigm detects whenever the fly enters the middle-vertical arm and consequently send the signal to the projector to illuminate that arm, which in turn reinforeces the fly behavior.
Below the validation tests at three different intensities (columns: intensities relative to projector max. output). I measured three different parameters for this validation (rows), for two conditions, with and without ATR (first and second graph). It seems like the paradigm affects both, number of entries and dwelling time within the arm. It seems like at the maximum intensity, even without ATR there might be an effect.
Droso Kurs and more
Here I attach the results in a pdf file from the students praktikum with an additional line I tested on my own meanwhile (Gr28bd and TrpA1 drivers together). They seem to work as a really good positive control btw, good for technique optimization.
For the students I tried out two different split drivers, the MB058B, which targets PPL1-a’2a2, and MB301B, which targets PAM-b2b’2. In addition the Gr5a driver, because it targets the “sugar” neurons. From the split drivers I wanted to see if I still get a validation from my initial model. MB301B seems to do quite what my model would predict but MB058B maybe not. Hopefully in a future screen I would be able to test many more and make a much more precise modelling.
Cumulative bins, starting at zero and normalizing
In the last meeting Björn proposed to do correlations of cumulative increasing bins. He said to do that taking the zeroth point (last library point where prediction is still not done) and use it for having a potential 1 of correlation coefficient at the beginning. I could not do that because I didnt save the zeroth points, and this will be a bit tedious and confusing considering that many flies were tested and probably the order is not 100% known. Thus, I just did the bins skipping this zeroth point. After all, we should see something similar with this one. First two graphs: c105;;c232>TNT (first and second prediction point), second: WTBxTNT, third: WTBxc105;;c232.
Examples of how each of the flies look like. So they are basically cumulative bins with each single fly (each in different colour). Just to have a hint how does the singularity looks like. 
Second thing I did is normalize the to have a range from -1 to 1 all of them (I have to double check the range in the script) and also setting them at a starting point of zero. I did this because we do not want to have differences in the correlation coefficient due to a different offset of the values of the wing beat and neither because of the starting point (if the fly was already flying to the right full gas, then it could be that it has an influence in the following prediction).
c105;;c232 –> first at starting at zero without normalizing and then with normalizing. The next is just the RMSE (not so important).
Prediction with binnning
To see if there is an exponential decay in the prediction of the fly traces we did correlations of bins of 40 data points. We have 4 graphs (the last one merged) which consist of predictions at two different points with two different number of neighbours used for the prediction. So we have for each group sucesively: prediction at the first prediction point with the first number of neigbours, then the same with different number of neighbours. The last two are two different numbers of neighbours for the second prediction point. In the order: c105;;c232>TNT, TNTxWTB, c105;;c232xWTB
Nonlinear signature of Drosophila in Strokelitude
This is now the results from trying to predict the fly behavior doing ensembles of two predictions for the next 200 data points at two different points of the traces.
c105;;c232>TNT:
WTBxTNT: 
WTBxc105;;c232: 
From what we see here, there is no “flattening” in the prediction of the fly when the neurons under c105 and c232 are targeted by TNT. This is done with around 14/15 flies for each group with two predictions in each ensemble of the two starting points. That makes a total of 15flies x 3 groups x 2 starting points for prediction x 2 predictions per ensemble = 180 prediction traces. Now I´m trying to calculate it by making correlations of bins in the prediction-observed for the same fly
Modelling the T-maze screen
This is the markdown showing the protocol and results of the modelling for the choice in the T-maze. This is for calculating valence. Nevertheless, this needs to be confirmed with the results of more lines, it could be that it is overfitted, I would like to do in addition cross-validation. I´m actually doing crosses and finding new lines to have more lines to test.
Fly behavior prediction in the platform
This is the prediction analysis of flies in the platform under a 20min experiment under dark conditions. The number of experiments change drastically among groups because of technical problems: WTBxTNT is 4, WTBxc105;;c232 is 22, for the experimental line is 6 (c105;;c232>TNT), for the platform without flies is 10. I show the root mean squares and the correlation coefficient for each group.
This is the experimental group: c105;;c232>TNT.
The group without flies on the platform. I expect here to get a very good predictability overall:
WTBxc105;;c232 group
WTBxUAS-TNT 
