## Recurrence quantitative analysis

on Monday, October 30th, 2017 11:43 | by

This is an example of a recurrence plot analysis. In the first graph is shown in single point in time in the optimal embedding dimension and the distance to the other points. For the recurrence plot analysis it is needed to put a threshold to make it binary. This is the second graph. From this second graph one can count many parameters like determinism, laminarity and so on. From what I see, the plots from the Strokelitude as well as Bjoern´s flight simulator in Maye et al 2007 show similar pattern (kind of crosses with vertical and horizontal lines).

This is a measure of the Recurrence Quantitative Analysis of different groups. Recurrence threshold is a tricky and to some extent subjective measure, so this is why I tried two different ones.

DET: recurrence points that form a diagonal line of minimal length, the more diagonal, the more deterministic.

LMAX: Max diagonal line length or divergence. Sometimes considered as an estimator of max. Lyapunov exponent

ENT: Shannon entropy reflects the complexity of the system

LAM: Laminarity is related to laminar phases in the system (intermittency). It is tallied as vertical lines over a threshold.

TT: Trapping time, measuring the average length of vertical lines. Related to laminarity.

Automat

One stripe

Openloop

Uniform

## More on the attractor

on Monday, October 16th, 2017 2:26 | by

Video of the attractor projected from a chunk of flight trace. Here one can see the difference in the trajectories of up- and down spikes. So here is another way of spike sorting by the way :).

Coloured traces depending where the locate in the attractor projection. Green means that they lie outside and when they stay with the PCs around zero its in red. The fly1r above does not look so clean as the v8 fly below (the cleanest measure I have)

## SMAP results

on Monday, October 9th, 2017 2:34 | by

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.

## Attractors for c105;;c232>TNT

on Monday, October 2nd, 2017 5:35 | by

This is what I showed about one fly from this line showing the attractor.

This graph is what I forgot to show in the lab meeting. There are the 6 best traces from this same line. All of them selected ad hoc subjectively. The three best of them to my eyes are exactly the three above in the graph (v8-this one is the one shown in the picture with the attractors-,v4,v2). What does this means? the ones with better traces (subjectively) have higher offset in the phi, this means that they are more predictable overall (maybe because more resolution?). In addition, they show higher slopes which means more nonlinearity.

## Cumulative bins, starting at zero and normalizing

on Monday, April 4th, 2016 3:04 | by

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

on Monday, March 21st, 2016 1:13 | by

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

on Monday, March 14th, 2016 1:48 | by

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

## Prediction analysis

on Sunday, January 17th, 2016 7:02 | by

These are just 5 flies (WTBxTNT) from the strokelitude where I measured the correlation coefficient on the Y-axis. In the X-axis, first bin is from 0-2 s of prediction, second is 2-4s and so on.

It seems as if some flies do nicer than others. Although it seems to me that a correlation coefficient from 0.3 isnt a big thing with all this variability. I have to find out the best binning though, I think it needs to be much more in the short term.

When I do the mean of the 5 flies measured, I do see a very slight decay. But once more I would say the decay is from the bin 1 to the second.

Here I tried another way, the RMSE, which according to literature and to my own reasoning should be a better analysis. I think RMSE measures just the differences of the absolute points whereas correlation coefficient is rather if the direction and degree of variation correlates (covariates). I find a very weird result. The fit is bad, the it gets better (but it should be just a chance event because correl coef decreases) and then it get very bad and so on.

I think for the future I have to make ensembles of two k neighbours maybe, which seem to increase the prediction power 10-15%. And maybe not look that much into the future as it was done here (10s).

Here some examples of predictions vs observations:

## Measuring Wingstroke Amplitude with Strokelitude (V)

on Monday, December 14th, 2015 2:42 | by

For a further data analysis, we should have a minimum number of samples. During the last week, I have been measuring the wingstroke amplitude of the flies, to get at least 10 samples of each one(two controls and the experimental line). Here three examples of the different lines:

Males WTB x C105;;C232 (control)

Males UAS-TNT-E x C105;;C232

Males UAS-TNT-E x WTB (control)

## Measuring Wingstroke Amplitude with Strokelitude (IV)

on Monday, December 7th, 2015 2:56 | by

After getting good results in the measurement of the wingstroke, and solving problems with the sampling intervals (image below). I have started to measure flies for the experiments.

The time between samples was different depending on the background programs running on the background:

Figure 1

More differences in time within samples when more programs are running at the same time as strokelitude (3nd 1/3 of the plot), when just the display of the camera is running( 1st 1/3 of the plot), and everything shut down(2nd 1/3 of the plot).

The differences among sampling intervals was bigger but with an adjustment of the data, Christian Rohrsen managed to changed. The time between two samples could arise until 1.7 seconds and with the correlation, the time is not bigger than 0.05 seconds.

The strains of flies used fore the experiments are:

UAS-TNT-E, that express the tetanus toxin in the neural cells, using GAL4 system. Being Used as a control.

c105;;c232, that contains a promotor region to express the toxin. Being used as a control.

And the cross between both to have the expression of the tetanus toxin.

Some data from the control flies (UAS-TNT-E):

And some data of the spikes: