## Finding the interesting lines

on Friday, July 20th, 2018 3:46 | by Christian Rohrsen

This is the correlation from the T-maze experiments from Gaia and Naman. Neither ranked nor regular correlation show any significant effect. This means that these effects seem to be random, at least for most of them, is this an overfitting result?

I would say blue 1 is a line that was negative for all the tests I have so far seen. So this might be an interesting line. What to do next?

I would unblind the blue1, which is TH-D’. It was shown to be required for classical conditionning in shock and temperature learning (Galili et al. 2014). Another interesting observation is that th-g4+th-g80 seems to have like zero PI scores in all of the experiments (Naman and Gaia in the Tmaze, Joystick and Y-mazes). So could it be that all of these neurons have indeed a meaning, but is depending every time in the context?? Maybe Vanessa Ruta´s work might be interesting for that.

## More on valence inference

on Monday, July 2nd, 2018 2:00 | by Christian Rohrsen

This is the linear model with its statistics

This is the linear model adding interactions. It is perfectly possible to have interactions between neurons, kind of what occurs with olfactory processing where ORNs activated alone or in different combinations have completely different meanings

I uploaded in slack the bayesian linear model with interactions. For any reason, it does not let me upload it now to the website

I am trying one of the ways of nonlinear models: GAM (Generalized additive models). Here one fit splines to the effects to certain degrees of freedom.

This is the kind of bar graphs I thought I could use for all the plots.

Category: neuronal activation, Operant reinforcment, Optogenetics | No Comments

## Modelling the valence of dopaminergic clusters from the Y-mazes

on Monday, June 25th, 2018 1:52 | by Christian Rohrsen

Dopaminergic clusters are differently targeted by the different Gal4s. Some of the express faintly, others stronger. Here I try to see if the dose-response curve (or expression-PI curve) seems to be linear or not. Here I put two examples from the 17 clusters, where the first two seem to have nonlinear curves, with and optimal expression level, and the last two seem to have a linear response curve.

This will be important for the modelling in order to decide to make a linear/nonlinear model. Down below I show the results from a linear model and it´s statistics. From Aso et al. 2012, one could see that activating the lines with TrpA1 shows a linear response curve. But in this case it does not necessarily seem to be the case. Therefore, light intensities might have an effect, as well as the expression level, and conclusion needs to be taken carefully.

In addition it is difficult to calculate this for all the clusters with just one single light intensity test, because not all clusters are expressed in several Gal4s to different level, so that we can estimate from there. So for the interesting lines we might need to make several experiments at different intensities, and see the dose response curve.

The G4s I have used for the modelling are the ones shown here.

## Making new ratios for Y-maze

on Monday, June 18th, 2018 1:55 | by Christian Rohrsen

This is just to show that I am trying to find a new ratio so that all graphs have from -1 to +1 ranges. That is why now the difference in occupancy time is divided by the total time. The same with the speed. Because speed differences are so subtle, the Y axis scale has to be lower

## Thornlabs spectrometer working!

on Monday, November 20th, 2017 2:59 | by Christian Rohrsen

After getting a new spectrometer, we confirmed that the first one was faulty. Comparison of old (1st and 3rd measures) and new (2nd and 4th) spectrometer. Now much more sensitive and the right spectrum measured for the green LED present in the spectrometer itself and for the light comming out of the light guide coupled with the red LED (whose spectrum does not seem to change after travelling through the light guide)

Category: neuronal activation, Operant reinforcment, Optogenetics | No Comments

## thf1 line from screen

on Monday, July 17th, 2017 2:40 | by Christian Rohrsen

This is the first line I completed from the screen. What it is more important is that I found out that NorpA;UAS-Chrimson has orange eyes, when it should be red. So I think I will start to do the line again and meanwhile test the contaminated line to see what the phenotype looks like

Category: neuronal activation, Operant reinforcment, Optogenetics, python code | No Comments

## Looking for students

on Monday, June 19th, 2017 6:01 | by Christian Rohrsen

Category: neuronal activation, Operant reinforcment, Optogenetics | No Comments

## Measuring locomotion in the Joystick? Something seems to happen…

on | by Christian Rohrsen

Example of ON/OFF traces. And then the wiggling was measured in both conditions (with and without light). It seems like they move more when they are in the light side. I also did measure the derivative with a tau=2 (in case the sampling is over the temporal frequency of fly behavior)

Category: neuronal activation, Operant reinforcment, Optogenetics, python code | No Comments

## Comparing T- and Y-maze

on Monday, June 12th, 2017 2:59 | by Christian Rohrsen

Comparing scores in both setups show some lines that match their scores and other lines that have opposite scores. This filters the ones that are context dependent to the ones that show a context independent reinforcement valence.

Category: neuronal activation, Operant reinforcment, Optogenetics | No Comments

## Optimizing the Joystick with Gr28bd+TrpA1>Chrimson

on Monday, May 29th, 2017 12:58 | by Christian Rohrsen

It seems that there is something more there. There were 5 batches of 3 flies per side reinforced. I put the intensity higher than ever before. I will try to get the maximum intensity for the next experiment and see what it looks like.

Category: neuronal activation, Operant reinforcment, Optogenetics, python code | No Comments