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:

I am trying to spot source of the current behavioral problem we are having. I tested some crosses that I know how they behave. I did the crosses in both directions and tested to different lights sources (cold and warm) at 25°C.

I started testing the different CS substrains that we have here (CS from the paper https://f1000research.com/articles/3-176/v1 + CS Regensburg). I’ve decided to start with Benzer Paradigm. This is what I have so far.

Updated:

https://figshare.com/preview/_preview/658786

It would be great to have some physiolgy data (electrophy) to have a guess about the effect of the PKC in the motorneurons (?)… Do you know if there is a paradigm to look at LTP and LTD at the MNJ?

I was on a meetup of corporate semantic web last Tuesday. These people are using semantic web technologies (making machine readable content based on ontological terms and relation between these terms) to improve the efficacy of private companies. For instance, they work on ways to improve wiki contents which may be produced in a company. This corresponds at using ontological term to annotate the wiki content and other related technologies. This can be used to find an expert in one category (=somebody who’s posts are rarely corrected on a specific subject).

What is the scientific community (the one which should be leading the way actually) doing during that time: we use text search in “keywords” and titles to find the appropriate literature, that we have to read thouroughly to drive our one conclusions about these different parts… At least, that is what we do 90% of the time, and we all know how inaccurate this can be. Experimental results may be translated into a machine readable content, why aren’t we doing it (it could make everything that much simpler, faster and more accurate)?

The answer: 1. there is no tool nor database where we could do it. 2. Scientists do not have the time to do it, they are over-pressurized to produce data, not to make it reusable or machine readable.
How to push people to use the semantic web technologies, how to ease this use, should it be done by the authors or by the community, pre or post publication, what ontology tool to use,… What can we do? Is anyone asking these questions around? Does a platform like researchgate be a way to introduce this, or should we go for a public solution, inside pubmed for example?

Is any of you asking/answering these questions?

By the way, this post is tagged by none-ontological terms, a shame?

https://figshare.com/preview/_preview/106910

reminder:
username colombj@zedat.fu-berlin.de

password: neurobio

definitive answer should come end of next week.

I took the poisson dataset from Maye’s published data and GRIP showed this result. However in the publication

yaxis range in poisson process is very low. I post the results of C105232 Gal4 lines in the next figure that shows

ISI close to poisson.

1-C105;c232Gal4-WTB,

2.C105;c232-TNT,

3.WTB-TNT

sorry for separate posts !

Yaw spike frequency here, seems TNT has some effect on it.

The following figure shows the computer generated correlated Buridan walk data. I extracted inter event parameters and used GRIP on them. We had plans to use this data as a control group., However it appears to be well above the poisson process. I really wonder what to use as control, I will generate may be poisson process myself within some xlimit, ylimit and can try,, Any ideas would be appreciated ?

Middle one is the TNT expressed on Gal4. Rest of them are controls; Y axis values are high than usual but highly variable as one would see in error bar.  0 value in the y axis would be the ideal randomness.

Can someone get me this article plz ?  sathish.r@fu-berlin.de

Chaotic Versus Stochastic Dynamics: A Critical Look at the Evidence for Nonlinear Sequence Dependent Structure in Dopamine Neurons

Thanks !