on Monday, December 4th, 2017 1:02 | by Christian Rohrsen
This is another way of showing how the slope of the SMAP analysis varies with length. I have choped the time series in 4 chunks and saw what was the slope for the chunks and for the whole time series. I think this is the best statistical way of doing it, it should not depend on what line was tested. I cannot see any effect.
Here below is just to show what I found out in the code. What I thought that theta was controlling for nonlinearity in the model, for me it seems rather a control for under- overfitting in the model. So I might need to read the paper and see what do they say, and if it is the same as what I found out in the code.
on Monday, November 27th, 2017 2:43 | by Christian Rohrsen
So this is the final graph assuming that 20Hz is the sampling rate. Sathish was not sure what it was and he said he will check.
To have a better overview how the length of the flight compares with the slope obtained from the SMAP. There is no big correlation whatsoever with around 100 flies. Sathish found this in his thesis with data from seventy something WTB, but to me it seems like an anecdotal result.
Here the same as above, just for showing the fit line.
on Monday, November 20th, 2017 1:47 | by Christian Rohrsen
This are the results of all of the flies analyzed from Sathish. I just need to know the frequency of the acquisition to exclude the ones that are below 6 minutes.
on Monday, November 6th, 2017 1:37 | by Christian Rohrsen
I have analyzed the c105+c232 > tnt data from Sathish. There are a total of 43 flies, although many of them only flew for a few minutes, and therefore should be discarded. Below all of the individual fly scores. I need to analyse now the other groups. This is btw the modified data set, whatever that means for Sathish.
This is a video of the projection from the torque data from Maye et al. 2007. The spikes are not that well sorted in this case as in the Strokelitude. I guess this is because the spikes do not look so smooth.
on Monday, October 30th, 2017 11:43 | by Christian Rohrsen
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
TND: info about stationarity (trend)
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.
on Monday, October 16th, 2017 2:26 | by Christian Rohrsen
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)
on Monday, October 9th, 2017 2:34 | by Christian Rohrsen
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.
on Monday, October 2nd, 2017 5:35 | by Christian Rohrsen
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.
on Saturday, February 11th, 2017 10:21 | by André Silva
This time I tried to induce a kind of Garcia effect (eating something, feeling sick and then avoiding eating that again) in Drosophila. As one can see in the video below, Drosophila show inhibited proboscis extension response (PER) right after the onset of the US (intestinal discomfort).
However, the conditioned response is extinguished after several hours. I tried to use groups of 20 starved flies on yeast+water for 2h, food + 0.1% hydrogen peroxyde for 1h, starving with nothing for 1h (always inside vials), and then testing with conditioned food against another food for 2h (inside Petri dishes). It didn’t work: in the end they show the same preference as controls (without hydrogen peroxyde). I tried to condition sucrose, glycine, pineapple juice (and then test against apple juice) and nothing, the flies avoid at first but once the swelling goes away, they forget or don’t associate anything bad or sufficiently bad with that food anymore, and continue to prefer it.
I also confirmed the PER suppression following conditioning with quinine reported in [C. Keene A, Masek P. Optogenetic Induction of Aversive Taste Memory. Neuroscience. 2012;222:173-180]. However, it is also extinguished minutes (~30) after the conditioning, as their results also show (in my case, I used sucrose instead of fructose):
on Thursday, February 2nd, 2017 9:49 | by André Silva
This time I tested how the behaviour changes with the proportion between the positive stimulus (sucrose) and the negative stimulus (quinine) and how it changes with the presence of a positive odour (vinegar) or a negative odour (benzaldehyde).