Positive Control: Gr28bd-G4, TrpA1-G4

Parameters: Light: intensity (500 Lux side, 1000 Lux bottom); frequency = 20Hz; Delay = 1 ms; Duration = 9.9 ms; volts = 6.4

Red lines: completed

mb025b: not selected against tubby

Mean trace of the positive control in the Joystick to get to see what are the overall dynamics and maybe to get an idea what might be the best score to pick.Here the standard deviation of the flies along the time axis. This is just to see if all the flies have more similar phenotypes with each other or not at each time.

This is to see if the flies have less degrees of freedom at any segment by measuring the standard deviation at each segment. There does not seem to be any effect. Although this might be mixed with the wiggle scores. I think measuring entropy is a better measure.

All the same plots as above but for TH-D’, the interesting line from the screen.

Standard deviation across flies

Standard deviation across segments

This are the performance indices for the different models performed to estimate the valence of the dopaminergic clusters. AIC: Akaike Information Criteria; BIC: Bayesian information Criteria; LogLikelihood: log Likelihood estimation

lm: linear model

+ int: taking double interaccions into consideration

b lm: bayesian linear model with bayesglm function

b lm MCMC: bayesian linear model with MCMCglm function

nlm: nonlinear model with lm function with splines fitted

b nlm: splines fitted to each cluster and MCMCglm function

GAM: general additive model with gam function

Adding double interactions seems to produce better models, nonlinearities also make models better and frequentist also. To me it seems like this  data might be noise and therefore adding interactions, nonlinearities and frequentist methods is just fitting the noise better (overfitting) and that is why I get better scores with them. In addition, care needs to be taken since I use different functions that calculate the model performance scores differently (although the formulas are theoretically the same for all!)

Yellow 1 (Positive Control): Gr28bd-G4, TrpA1-G4

Parameters: Light: intensity (500 Lux side, 1000 Lux bottom); frequency = 20Hz; Delay = 1 ms; Duration = 9.9 ms; volts = 6.4

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.

Results of the T-maze screen analysis, both individual and combined.

Yellow 1 (Positive Control): Gr28bd-G4, TrpA1-G4

Parameters: Light: intensity (500 Lux side, 1000 Lux bottom); frequency = 20Hz; Delay = 1 ms; Duration = 9.9 ms; volts = 6.4

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 .

Yellow 1 (Positive Control): Gr28bd-G4, TrpA1-G4

Parameters:
Light: intensity (500 Lux side, 1000 Lux bottom)

frequency = 20Hz

Delay = 1 ms

Duration = 9.9 ms

volts = 6.4

plotting the mean ratio of flies which stay in the middle during experiment.

Yellow 1 (Positive Control):  Gr28bd-G4, TrpA1-G4

Parameters:
Light: intensity (500 Lux side, 1000 Lux bottom)

frequency = 20Hz

Delay = 1 ms

Duration = 9.9 ms

volts = 6.4

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.