Figures and data in Variance adaptation in navigational decision making

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Ruben Gepner

Jason Wolk

Digvijay Shivaji Wadekar

Sophie Dvali

Marc Gershow

(2018)
Variance adaptation in navigational decision making

eLife 7:e37945.

https://doi.org/10.7554/eLife.37945
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Figures
Tables
Additional files

9 figures, 2 tables and 1 additional file

Figures

Figure 1 with 2 supplements

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Reverse correlation analysis of variance adaptation.

(a) Linear-Nonlinear-Poisson (LNP) model of the decision to turn. Sensory input is processed by a linear filter to produce an intermediate signal. The rate at which the larva initiates turns is a …
https://doi.org/10.7554/eLife.37945.002

Figure 1—figure supplement 1

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Overview of analysis steps.

Overview of analysis steps Graphical demonstration of the calculation of the visual rate functions in Figure 1e (a) Example data from 1 cycle of one visual variance adaptation experiment. Top: led …
https://doi.org/10.7554/eLife.37945.003

Figure 1—figure supplement 2

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Rate functions following single filter vs variance-specific filters.

Rate functions following single filter vs. variance-specific filters Rate functions of Figure 1e re-calculated using filters derived from high and low variance turn-triggered averages. (**a,b**) Berlin …
https://doi.org/10.7554/eLife.37945.004

Figure 2

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Comparing rescaling models of variance adaptation.

(a) Best fit rate functions to visual (Berlin with blue light stimulus) data of Figure 1, for various functional forms of rescaling, and for a null model with no rescaling. $Δ B I C$ indicates difference …
https://doi.org/10.7554/eLife.37945.005

Figure 3

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Variance adaptation and temporal dynamics of input rescaling.

Larvae were exposed to alternating 20 s periods of high and low variance intensity derivative white noise. For Berlin, the stimulus was visual (blue light). For all other genotypes, the stimulus was …
https://doi.org/10.7554/eLife.37945.006

Figure 4 with 1 supplement

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Variance adaptation to a stimulus with uncorrelated values.

Berlin wild type animals were exposed to blue light. Every 0.25 s, the intensity of the light was chosen from a random normal distribution with fixed mean and low or high variance. (a) …
https://doi.org/10.7554/eLife.37945.007

Figure 4—figure supplement 1

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Comparison of stimulii with uncorrelated random derivatives and uncorrelated random values.

Comparison of stimulii with uncorrelated random derivatives and uncorrelated random values Left: uncorrelated random derivatives. (a) Light levels (blue, below) and difference between subsequent …
https://doi.org/10.7554/eLife.37945.008

Figure 5

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Input rescaling as a function of variance.

Larvae were exposed to intensity derivative white noise whose standard deviation steadily increased and decreased in a 120 s period triangle wave. Top row: visual stimulus - blue light was presented …
https://doi.org/10.7554/eLife.37945.009

Figure 6

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Optimal variance estimator predicts input rescaling.

We generated an estimate of the input variance using a Bayes estimator that sampled the stimulus at an interval of $Δ t$ , with a prior $τ$ that represented the expected correlation time of environmental …
https://doi.org/10.7554/eLife.37945.010

Figure 7

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Multisensory variance adaptation and temporal dynamics of input rescaling.

Or42a $>$ CsChrimson larvae were exposed to both visual (dim blue light) and fictive olfactory (red light) stimuli with random intensity derivatives. *Top row*: visual input had constant variance, while …
https://doi.org/10.7554/eLife.37945.011

Figure 8 with 2 supplements

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Multisensory combination models.

(a) Indepdendent pathways: two independent LNP models transform odor and light stimuli into decisions to turn; these turn decisions are combined by an OR operation at a late stage. This model is …
https://doi.org/10.7554/eLife.37945.012

Figure 8—figure supplement 1

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Reanalysis of white noise experiments.

Reanalysis of white noise experiments (a) Results from Gepner et al. (2015). Left: turn-triggered average changes in visual stimulus (blue) and optogenetic activation (red) for entire data set. …
https://doi.org/10.7554/eLife.37945.013

Figure 8—figure supplement 2

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Reanalysis of multisensory step experiments.

Reanalysis of multisensory step experiments Measured and fit turn rate for various combinations of favorable and unfavorable changes in odor and light stimuli. $Δ B I C$ indicates difference in Bayes …
https://doi.org/10.7554/eLife.37945.014

Figure 9

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Adaptation to variance in a natural odor background.

(a) Optogenetic activation of odor receptor neurons with a fluctuating background of carbon dioxide. Above: Noise of constant variance was provided to either the CO $_{2}$ receptor neuron (Gr21a $>$ CsChrims…
https://doi.org/10.7554/eLife.37945.015

Tables

Key resources table

Reagent type (species) or Resource	Designation	Source or reference	Identifiers
Strain (Drosophila melanogaster)	Berlin wild type	gift of Justin Blau, NYU
Genetic reagent (D. melanogaster)	w1118;;20XUAS- CsChrimson-mVenus	Bloomington Stock Center	RRID:BDSC_55136
Genetic reagent (D. melanogaster)	w*;;Gr21a-Gal4	Bloomington Stock Center	RRID:BDSC_23890
Genetic reagent (D. melanogaster)	w*;;Or42a-Gal4	Bloomington Stock Center	RRID:BDSC_9969
Genetic reagent (D. melanogaster)	w*;;Or42b-Gal4	Bloomington Stock Center	RRID:BDSC_9972
Genetic reagent (D. melanogaster)	w*;;Or35a-Gal4	Bloomington Stock Center	RRID:BDSC_9968
Genetic reagent (D. melanogaster)	w*;;Or59a-Gal4	Bloomington Stock Center	RRID:BDSC_9989
Software, algorithm	MAGATAnalyzer	(Gershow et al., 2012) github.com/samuellab/MAGATAnalyzer-Matlab-Analysis/	d9d72b2b43c82af...

Table 1

Number of experiments, animals, turns.

# experiments - number of 20 min duration experiments; a different noise input was used for each experiment within a group; # animals - estimate of total number of individual larvae surveyed in each …

https://doi.org/10.7554/eLife.37945.016

Figure	Genotype	# expts	# animals	animal-hours	# turns
Figure 1,Figure 2	Berlin	17	811	219.3	48711
Figure 1,Figure 2	Or42a $>$ CsChrimson	17	743	201.1	33822
Figure 3	Berlin	30	1087	302.4	55776
	Or42a $>$ CsChrimson	19	838	247.5	44806
	Or42b $>$ CsChrimson	15	600	163.3	23826
	Or59a $>$ CsChrimson	15	723	177.6	16418
	Or35a $>$ CsChrimson	11	430	121.1	13149
	Gr21a $>$ CsChrimson	19	786	230.1	36121
Figure 4a,b	Berlin	16	483	133	22741
Figure 4c	Berlin	18	699	189	28226
Figure 5	Berlin	10	372	102.5	18397
Figure 5	Or42a $>$ CsChrimson	13	553	147.2	21897
Figure 7	Or42a $>$ CsChrimson
Odor switches variance, visual constant		6	165	46.1	6772
Odor constant variance, visual switches		10	391	105.4	15210
Correlation switches		16	616	156.8	22142
Figure 9a	Gr21a $>$ CsChrimson
High Variance CO $_{2}$		6	242	66.2	11286
Low Variance CO $_{2}$		6	242	62.4	10534
Figure 9a	Or42a $>$ CsChrimson
High Variance CO $_{2}$		7	286	80.5	12976
Low Variance CO $_{2}$		7	276	77.9	12096
Figure 9b	Gr21a $>$ CsChrimson
High Variance CO $_{2}$		3	121	33.2	7289
Low Variance CO $_{2}$		3	116	32.7	7023
Figure 9b	Or42a $>$ CsChrimson
High Variance CO $_{2}$		2	94	25	5174
Low Variance CO $_{2}$		2	82	22.4	4571