Johann Schmid and I have been working on getting updates to the torque meter software. Small changes but with significant increase in user friendliness.

● Inability to overwrite the data

● Progress bar and a time bar implemented

● It resets the pattern from one period to another. This is of critical importance as this enables one to do basically any kind of experiment on the machine

I have also gotten hold of a free version of LabView. I thought it could be a good idea that we could to small changes ourself to the software. However, my version is 2017 and Mr Schmid mentioned that he will be transferring to LabView 2019, and thereby retiring the 2017 version. A student version of labview is affordable, less than 50 Euros. It could be worth getting a legal licence of this software.

● The A/D converter now connects directly to the PCB. Only problem is that it is inverted, meaning that the signal from the torque machine gives a positive signal it is registered as a negative signal in the software. Mr Schmid is aware of this and will invert the signal in the software, rather than resoldering the PCB connection.

Updates to the DTS

● DTS is now also better compatible wit the new kind of data we are getting. A conflict occurred because of differences in pattern. An easy fix by just ignoring this parameter.

This week we worked on perfecting the fly hooking. Some of the data we got is as follows (Data from just one fly).

The mean ratio of the flies that stay in the middle during the experiments.

Correlation plot between the mean ratio of the flies that stay in the middle versus the Weighted PIs

Slope = 0.0053
Intercept = 0.240
R square value = -0.03834

contrary to the expectations, there seems to be no correlation .

PKG 2_descr_anal

wtb-rut-colorlearning

Replicated wild type berlin flies in the flight simulator

The results from wiltype Berlin flies in the flight simulator.

Trace segment from Maye et al. 2007 in the uniform arena

 

Trace after filtering by selecting the first 8 IMFs (intrinsic mode functions) from EMD (empirical mode decomposition). Since the signal should be quite clean I do not take out the first IMFs. The last IMFs, however, are too slow and change the baseline to much

Some examples of the IMFs obtained from EMD:

Since this is separating behavior adapting to the data intrinsic time scales I am now thinking of analysing with the SMAP algorithm to see if the behavior is more or less nonlinear at certain time scales.

In addition, I have thought of using ICA (Independent component analysis), that is an algorithm famous for the blind source separation problem by extracting the most independent signals from the input signals (in this case the IMFs which are different time scales of the behavior). So the ICs should consist of mixes of different time scales that are correlated together and thus belong (but not necessarily) to the same action/movement module/… Here a few ICs (from 10). My idea is that muscles might coordinate independently between ICs but coordinated within ICs. However to prove that is not that easy I guess

These are the results of the SMAP for the TNTxWTB. I also have done a few for the c105;;c232xWTB but there is not much to say. I would say that the cleanest lines show a bigger slope, but prone to subjectiveness.

In addition, I have done some animations of the attractors that I have posted on slack because of size.

praktikumandmine

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.

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.

WTBxTNT

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).