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Neural Mechanisms for Ev
aluating EnvironmentalVariability in Caenorhabditis elegansHighlights
d C. elegans learn variability in their food environment and
modify future behavior
d The authors describe the minimal circuit that evaluates
environmental variability
d Dopamine levels store information about variability to
regulate behavior
d The amount of CREB determines the rate of learning
variability
Calhoun et al., 2015, Neuron 86, 1–14April 22, 2015 ª2015 Elsevier Inc.http://dx.doi.org/10.1016/j.neuron.2015.03.026
Authors
Adam J. Calhoun, Ada Tong, ...,
Tatyana O. Sharpee,
Sreekanth H. Chalasani
In Brief
How does an animal evaluate variability in
its environment? Calhoun et al. describe
how C. elegans sensory circuits learn
about food variability and use that
information to drive a long-lasting
behavior in a dopamine-dependent
manner.
Neuron
Article
Neural Mechanisms for Evaluating EnvironmentalVariability in Caenorhabditis elegansAdam J. Calhoun,1,2,3,6 Ada Tong,2 Navin Pokala,4 James A.J. Fitzpatrick,5 Tatyana O. Sharpee,1,3
and Sreekanth H. Chalasani1,2,*1Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA2Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA3Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA4HowardHughesMedical Institute, Lulu and AnthonyWang Laboratory of Neural Circuits and Behavior, The Rockefeller University, NewYork,
NY 10065, USA5Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA6Present address: Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
*Correspondence: [email protected]
http://dx.doi.org/10.1016/j.neuron.2015.03.026
SUMMARY
The ability to evaluate variability in the environment isvital for making optimal behavioral decisions. Herewe show that Caenorhabditis elegans evaluatesvariability in its food environment and modifies itsfuture behavior accordingly. We derive a behavioralmodel that reveals a critical period over which infor-mation about the food environment is acquired andpredicts future search behavior. We also identify apair of high-threshold sensory neurons that encodevariability in food concentration and the downstreamdopamine-dependent circuit that generates appro-priate search behavior upon removal from food.Further, we show that CREB is required in a subsetof interneurons and determines the timescale overwhich the variability is integrated. Interestingly, thevariability circuit is a subset of a larger circuit drivingsearch behavior, showing that learning directly mod-ifies the very same neurons driving behavior. Ourstudy reveals how a neural circuit decodes environ-mental variability to generate contextually appro-priate decisions.
INTRODUCTION
Neural circuits respond to changing sensory environments and
generate appropriate behaviors by integrating prior experience
with new information. In particular, the variability of the environ-
ment plays a crucial role in shaping an animal’s behavioral strat-
egy, as unpredictable environments lead to unpredictable
reward. When presented with a choice, animals prefer a behav-
ioral strategy that will generate stable reward rather than uncer-
tain reward (MacLean et al., 2012; Platt and Huettel, 2008).
Selecting an uncertain rewardmay lead to dangerous outcomes.
For example, in the case of food, no reward (absence of food)
represents possible starvation (Watson, 2008). Thus, animals
devote considerable resources to determining reliability in their
environment (Escobar et al., 2007; MacLean et al., 2012). In
general, a successful behavioral strategy requires knowledge
about the variability in the environment (Tishby and Polani,
2011). Evaluating variability can be particularly challenging, as
the underlying neural circuit must learn and remember the vari-
ability in reward over different timescales.
In the visual system, the rate and statistics of action potential
firing encode information about rapidly occurring variations in
stimuli. Adaptation to variability in visual stimuli usually occurs
within seconds (Ozuysal and Baccus, 2012). Moreover, the
speed of resolving ambiguities approaches the physical limits
imposed by the sampling rate and noise of individual neurons
(DeWeese and Zador, 1998; Fairhall et al., 2001; Wark et al.,
2009). However, little is known about how the neural circuit eval-
uates reliability over minutes to generate complex behaviors
that last many minutes (Monosov and Hikosaka, 2013). We
approach this problem by studying how environmental variability
affects decision making in Caenorhabditis elegans. Its relatively
small nervous system, which consists of only 302 neurons with
identified synaptic connections, is ideally suited for identifying
neural mechanisms at the resolution of single cells and circuits
(Chalasani et al., 2007; Chalfie et al., 1985; de Bono and Maricq,
2005; White et al., 1986). Moreover, specific food-related
behaviors and their associated neuronal circuits are well-
characterized. For example, C. elegans removed from bacterial
food execute an initial local search of a restricted area for about
15 min by interrupting forward movements with seemingly
random reorientations (turns). After 15 min, animals disperse
by suppressing the reorientations and execute a global search
of a larger area (Gray et al., 2005; Hills et al., 2004; Wakabayashi
et al., 2004). The local followed by global search strategy is
similar to one adopted by ants returning to their nest (Lanan,
2014; Merkle and Wehner, 2008; Wehner et al., 2006). Given
the similarity in behavioral search strategies, we suggest that
the underlying neural mechanisms identified in C. elegans are
relevant across many species.
Cell ablation experiments identified a 48-neuron circuit in
C. elegans consisting of 6 sensory neurons, 16 interneurons,
and 26 motor neurons that regulates local search behavior
Neuron 86, 1–14, April 22, 2015 ª2015 Elsevier Inc. 1
Please cite this article in press as: Calhoun et al., Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans, Neuron(2015), http://dx.doi.org/10.1016/j.neuron.2015.03.026
Flu
ores
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4 Large patchSmall patch
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on food
off food)
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Normalized distance from edge
Der
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atch
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tch
Figure 1. Worms Exploring Differently Sized Food Patches Experience Different Sensory Environments
(A) Cross-section of a bacterial patch expressing green fluorescent protein (GFP) shows higher concentrations on the edge compared to the center.
(B) The spatial derivative from the edge to the center of the patch shows a steep edge gradient and constant center across a range of patch sizes (n = 5 for each
size). Lines represent population mean, and shaded areas are SEM.
(C) Worms on food show a lower residence time on the edge of the patch when taken from small patches (small patch, n = 28; large patch, n = 32; t test **p < 0.01).
Bars are population means, and error bars are SEM.
(D) A Markov model was run to simulate worm movement across a bacterial patch (see also Supplemental Experimental Procedures). The observed bacterial
concentration was more variable for small patches (top right) compared to those on large patches (bottom right).
(legend continued on next page)
2 Neuron 86, 1–14, April 22, 2015 ª2015 Elsevier Inc.
Please cite this article in press as: Calhoun et al., Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans, Neuron(2015), http://dx.doi.org/10.1016/j.neuron.2015.03.026
(Gray et al., 2005). Since this search circuit is characterized, we
focused on whether environmental variability modulates local
search behavior and its underlying neural circuit. We found
that only a subset of the circuit defined above is involved in
evaluating variability. The smaller circuit, which consists of
4 sensory neurons and 12 interneurons, evaluates food vari-
ability and modifies local search behavior. Further, dopamine
signaling is required to encode food variability, influencing
both sensory neurons and interneurons. We used a combina-
tion of genetics, behavioral analysis, calcium imaging, and
theoretical methods to investigate how a neural circuit evalu-
ates environmental variability to determine contextually appro-
priate behavior.
RESULTS
Defining a Variable Sensory ExperienceC. elegans feeds on bacteria and is typically cultured in the
lab on plates with E. coli patches (Brenner, 1974). We hypoth-
esized that animals experience changing environments as they
traverse a bacterial patch. To gain insight into an animal’s sen-
sory experience, we analyzed the gradients of fluorescence
across a bacterial patch expressing green fluorescence pro-
tein (Labrousse et al., 2000) (Figure 1A). Using fluorescence in-
tensity as a proxy for bacterial concentrations, we found that
patches of different sizes have similarly steep gradients at
the edge and are flat in the center of the patch (Figures 1B,
S1A, and S1B). We then analyzed the behavior of animals
exploring patches of different sizes. We found that animals
on small patches (7.4 mm diameter) spend more time at the
edge than animals on large patches (19 mm) (Figures 1C,
S1C, and S1D).
We hypothesized that animals on small patches aremore likely
to be at the edge because the relative edge size (ratio of circum-
ference to surface area) of a lawn is greater for small patches
compared to large patches. We verified the effect quantitatively
with a simple Markov model, using empirically obtained data
(speed and size of animal) to simulate random movement on
food patches of different sizes. The location probabilities pre-
dicted by the simulation matched observed positions of animals
exploring patches (compare Figures 1C and S1E). Our simulation
also confirmed that animals exploring small patches are more
likely to encounter edges compared to those exploring large
patches. Consequently, animals exploring a small patch
encounter large changes in bacterial variability more frequently
than those exploring a large patch (Figures 1D and S1). These
data suggest that animals exploring differently sized food
patches experience differing levels of food variability. Small
patch exposure results in a more frequently changing food expe-
rience, while large patch exposure results in a more constant
experience.
Variable Sensory Experience Modifies Local SearchBehaviorTo test whether the differences in ‘‘on-food’’ sensory experience
modify subsequent ‘‘off-food’’ behavior, we analyzed the
response of animals that explored differently sized bacterial
patches in the food search assay (Figure 1E) (Gray et al., 2005;
Wakabayashi et al., 2004). We observed that local search was
modified by prior on-food experience. Specifically, the fre-
quency of reorientations an animal executes during local search
was directly proportional to the size of the patch from which it
was removed. For example, experiencing a small patch results
in about 25% fewer turns (Figures 1F, 1G, and S2A). This expe-
rience specifically modified the number of large reorientations in
locomotory paths caused by large-angled turns, but not the
number of non-reorienting reversals (Figures S2B–S2D). These
data further indicate that patch experience only affects behavior
relevant to local search, but not overall locomotion (Figure S2E).
Moreover, the patch size only influences local search behavior
(2–15 min off food) but not global search (20–30 min off food)
(Figures 1F–1G, Table S1). In particular, we found that animals
continued to exhibit patch-size-regulated change in local search
(plasticity) even on patches of dead bacteria (see Experimental
Procedures, Figure S2H, Table S1). The patch-size-dependent
plastic behavior was not specific to E. coli patches, as animals
also displayed similar behavioral responses after exploring
patches of Comamonas sp. or Pseudomonas fluorescens (Fig-
ure S2G, Table S1).
To test whether this patch-size-dependent behavior occurs in
the wild, we analyzed the behavior of wild isolates. We found that
CB4856 (Hawaiian), AB1 (Australian), and RC301 (German) iso-
lates all displayed patch-size-dependent search, suggesting
that this might be an ecologically relevant behavior (Figure S3A,
Table S1). We also found that RC301 animals showed signifi-
cantly more patch-size-dependent plasticity when compared
to other wild isolates (Figure S3A, Table S1). The data suggest
that various C. elegans strains use information about food expe-
rience to modify local search as a general strategy for exploring
their environment. Collectively, these results indicate that ani-
mals exploring different patch sizes experience different levels
of variability in their sensory environment and use that informa-
tion to modify local search behavior.
The On-Food Sensory Experience May Be Influenced byEating and Oxygen-Sensing BehaviorsDifferences in bacterial density between the edge and center of a
bacterial patch (Figures 1A and 1B) may modify off-food search
in several ways. To test the possibility that an animal consumes
different amounts of food at the edge and at the center, we
analyzed the behavior of eating-deficient mutants, eat-2 (McKay
et al., 2004), in our behavioral paradigm. We found that eat-2
mutants were unable to distinguish between small and large
(E) Animals were removed from small and large patches of bacteria, and their local search behavior was analyzed. Example trajectories of animals removed from
small and large lawns during the first 2–5 min of their local search (right).
(F) Animals removed from a large patch (red) execute more turns during local search (2–15 min) when compared to animals from a small patch (black,
Kolmogorov-Smirnov test, ***p < 10�4). Global search (20–28 min) is not affected by patch size (n R 62).
(G) Bars represent a population mean, and error bars and shaded areas are SEM. In this figure and Figures 7, S2, S4, and S7, search behavior is shown as
‘‘Reorientations/min.’’
Neuron 86, 1–14, April 22, 2015 ª2015 Elsevier Inc. 3
Please cite this article in press as: Calhoun et al., Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans, Neuron(2015), http://dx.doi.org/10.1016/j.neuron.2015.03.026
patches, suggesting that eating behavior affects local search
(Figure S3B, Table S1). To directly assess eating behavior, we
measured the number of pharyngeal pumps that an animal
made on the edge as opposed to the center of a patch. An ani-
mal’s pumping rate is directly proportional to the amount of
food it consumes (Avery and You, 2012). We found that animals
pumped significantly more on the edge (average = 270.9 pumps/
min; SEM = 9.19) compared to those on the center (average =
203.2 pumps/min; SEM = 11.49). Thus, an animal is likely to
consumemore bacteria on the edge when compared to the cen-
ter, and this difference might affect local search.
We also tested whether differences in oxygen or carbon diox-
ide concentrations between the edge and the center of a bacte-
rial patch influence local search. Animals with mutations in
soluble guanylate cyclase gcy-35 are unable to sense oxygen
gradients (Gray et al., 2004). gcy-35 mutants were unable to
distinguish between small and large patches and performed
similar local search after removal from both patches (Figure S3C,
Table S1). This result suggests that oxygen sensing is also
required to generate patch-size-dependent local search. By
contrast, receptor guanylate cyclase gcy-9 mutants that cannot
sense carbon dioxide gradients (Hallem et al., 2011) can distin-
guish between small and large patches (Figure S3C, Table S1).
Collectively these results indicate that differences in bacterial
consumption and oxygen sensing contribute to the on-food
experience and alter local search behavior.
A Critical Period of Patch Experience Predicts LocalSearchWe hypothesized that animals use the sensory experience ac-
quired during patch exploration to modify their future local
search behavior. To identify features of patch sensory experi-
ence that best predict the number of reorientations executed
during local search, we monitored the on- and off-food behavior
of animals. We tracked the tip of an animal’s nose as it explored
the food environment and then later analyzed local search
behavior. We found that the frequency of movement between
the edge of a bacterial patch and the center, i.e., movement be-
tween regions of high and low bacterial concentrations, ac-
counts for a large fraction of the difference in local search
behavior (Figures 2A, 2C, and S4). We also tested other possible
predictors of behavior, including time spent off the food patch or
movements on the center of a patch, and found that none of
them could predict reorientations during local search (Figure S4).
These results suggest that animals may evaluate a patch by their
locomotory paths between the edge and center and use this in-
formation to modify local search.
Next, we attempted to identify a critical time period of the on-
food patch experience that modifies off-food local search. We
used maximum noise entropy (MNE) (Fitzgerald et al., 2011) to
extract a filter that identified on-food time points relevant to
modifying off-food search. In particular, the filter maximizes un-
certainty across trials in order to be consistent with the known
input/output relationships but makes no other assumptions
(see also Experimental Procedures). The filter reveals a critical
on-food period that predicts off-food local search behavior—
the period from 5 to 25 min preceding removal from food
accounts for a large degree of the difference in the number of re-
orientations each animal will make after removal from food (Fig-
ure 2A). By contrast, neither the immediate 5 min preceding
removal from food nor any time prior to 30 min before removal
predicts future behavior. Notably, using the position of the
nose gave a significantly better prediction than using the center
of mass of the body (data not shown). Other variables, such as
time spent off food and change in position while not on the
edge (the change in the set of all the positions excluding time
on edge), showed no relevance to behavior (Figures S4B–S4D).
Our model based on the filter above predicted that an animal
transitioning more frequently between the edge and the center
of the patch would execute fewer reorientations during local
search. Given that animals exploring small patches transition be-
tween the edge and center more often than animals on large
patches (Figure 1C), the model accounted for the observation
that animals removed from small patches reorient less than ani-
mals removed from large patches. Themodel also predicted that
individual animals on similarly sized food patches would reorient
a different number of times if they have different numbers of edge
encounters. We modeled the behavior of animals with a differing
number of edge encounters and found that those that transi-
tioned more frequently between the edge and the center of the
lawn (Figure 2Biii) made fewer turns after removal from food
during local search (Figure 2Biv). Thus, our model can broadly
account for differences in the number of turns performed by in-
dividual animals based on their on-food experience (Figure 2C).
We designed an additional assay to confirm that the variability
that an animal experiences on food affects its local search
behavior. We increased the variability of the patch, but not total
amount of bacteria in the patch, by adding bumps (see Experi-
mental Procedures, Figure 2D, Table S1). We found that when
animals explored small bumpy patches with increased vari-
ability, they executed 20% fewer turns compared to regular
small patches (Figure 2D, Table S1). Interestingly, increasing
the variability in a large patch did not strongly affect the local
search (Figure 2D, Table S1). These results show that animals
integrate the variability of their sensory environment 5–25 min
prior to food removal and then use that information to drive local
search behavior.
A Subset of Sensory Neurons, ASI and ASK AreSpecialized for Evaluating Patch VariabilityPrevious studies identified three pairs of sensory neurons (ASI,
ASK, and AWC) that drive local search behavior (Gray et al.,
2005;Wakabayashi et al., 2004). All of these neurons extend their
dendrites to the nose of the animal, where they detect environ-
mental changes and control behavior (Gray et al., 2005; White
et al., 1986) (Figure 3A). We examined the role of ASI, ASK,
and AWCneurons in learning the variability of the bacterial patch.
We compared the reorientations/min executed by wild-type
animals during local search when they are removed from small
or large patches. We found that animals removed from large
patches have a 20% increase in their reorientations/min
compared to those removed from small patches (Figure 3B,
Table S1). Interestingly, blocking neurotransmitter release by ex-
pressing tetanus toxin light chain fragment (TeTx) (Schiavo et al.,
1992) in ASI and ASK, but not AWC, sensory neurons disrupted
patch-size-dependent search. Moreover, AWC-ablated animals
4 Neuron 86, 1–14, April 22, 2015 ª2015 Elsevier Inc.
Please cite this article in press as: Calhoun et al., Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans, Neuron(2015), http://dx.doi.org/10.1016/j.neuron.2015.03.026
alsomaintained their ability to distinguish between the two patch
sizes, confirming that the AWC sensory neurons are not required
for integrating patch size into local search behavior (Figure 3B,
Table S1).
Glutamate is the major excitatory neurotransmitter in the
C. elegans nervous system and has been shown to play a crucial
role in regulating local search behavior (Chalasani et al., 2007).
To test whether glutamate release from sensory neurons was
required to achieve plasticity in local search, we analyzed the
behavior of signaling mutants. EAT-4 is the C. elegans homolog
of the vesicular glutamate transporter, and its loss results in
severe defects in multiple behaviors including food search
Observed reorientations
Mod
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C
Minutes prior to start of search
Con
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r=0.80, p<1x10-4
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Large Large &Bumpy
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Figure 2. A Filter Predicts the Number of Turns during Local Search
(A) A filter representing the contribution of the dispersion in movement orthogonal to the edge predicts that this movement is important between 5 to 25 min prior
to removal from the patch for determining turns during local search. Lines represent mean of four jack-knives (see also Experimental Procedures), and shaded
area represents SEM.
(B) (i, iii) Example tracks of animals exploring similarly sized patches. The animal exploring the top patch (red *) generates more turns during local compared to the
animal exploring the bottom patch (red ,). (ii, iv) Comparing predictions from the filter to observed total number of turns.
(C)Model prediction provides a good fit of actual off-food search behavior for individual animals (r = 0.8, p < 10�4). Data from the example tracks are indicatedwith
a red * and ,.
(D) Animals were allowed to explore small and large patches as well as patches where denser food was placed in the center of the patch. Animals exploring small
patches with large bumps performed significantly fewer turns than small patches without bumps (n R 30). Bars represent population means, and error
bars represent SEM. * indicates significantly different population means compared to behavior of animals exploring small patches (Kolmogorov-Smirnov test,
p < 0.05). Significance was also corrected for a false discovery rate of 0.05.
Neuron 86, 1–14, April 22, 2015 ª2015 Elsevier Inc. 5
Please cite this article in press as: Calhoun et al., Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans, Neuron(2015), http://dx.doi.org/10.1016/j.neuron.2015.03.026
(Chalasani et al., 2007; Lee et al., 1999). Restoring EAT-4 in indi-
vidual neurons did not rescue patch-size-driven plasticity but
restoring EAT-4 to both ASI and ASK neurons rescued the ability
to distinguish patch size (Figure 3C, Table S1). However, we
found that restoring function of the EAT-4 transporter to any
one of AWC, ASI, or ASK individually rescued the mutant’s
inability to transition from a local to a global search (Figure 3D).
Interestingly, we found that rescuing eat-4 in both ASI and ASK
neurons increased the number of reorientations throughout the
entire assay (Table S1), suggesting that glutamate release from
ASI and ASK influences the duration of local search at the
expense of transition to global search (Figure 3D).We also tested
various glutamate receptormutants and found that AMPA recep-
tor glr-1, glutamate-gated chloride channels glc-1, glc-2, glc-3,
and glc-4, and metabotropic receptors mgl-1, but not mgl-2,
were required for the patch-size-driven plasticity (Table S1).
Our mutant analyses indicate a complex role for glutamate
signaling in driving local search behavior. These results show
that glutamate release from any of the three neurons, ASI,
ASK, or AWC, can drive local search behavior but that release
from both ASI and ASK is necessary and sufficient for patch-
size-driven local search. Our results also highlight a key differ-
ence between the circuit that generates local search and the
circuit controlling its behavioral plasticity.
To further investigate how ASI and ASK neurons detect
variability of food stimuli, we used calcium imaging to analyze
sensory neuron activity in response to different concentra-
tions of bacterial stimuli in transgenic animals expressing
the GCaMP calcium sensor (Tian et al., 2009). We used a
custom-designed microfluidics device that allowed us to trap
animals, deliver precisely timed stimuli to the nose, and record
neural activity (Chalasani et al., 2007; Chronis et al., 2007). An-
imals exploring a food patch on a plate experience a 10-fold
change in bacterial stimuli when moving between the center
and edge of the bacterial patch (Figure S1B). Thus, for these
imaging experiments, we presented stimuli that represented
a similar fold change and recorded neuronal responses (see
Experimental Procedures). Consistent with previous results
(Chalasani et al., 2007), we found that AWC neurons responded
to the removal of food stimuli in a dose-dependent manner
(Figures 4A and S5A). In contrast, ASK sensory neurons re-
sponded to the removal of large, but not small, concentrations
of food stimuli (Figures 4B and S5B). ASI neurons responded
to the addition of large, but not small, changes in stimuli (Fig-
ures 4C and S5C). Next, we tested whether the neurons
encode absolute or relative changes in food stimuli. We found
that both ASI and ASK neurons responded to a 10-fold, but
not a 2-fold, change in food stimuli, suggesting that they
signal relatively large changes in stimuli (Figures 4D, 4E, S5D,
and S5E).
We have shown that animals experience large changes in
their food environment when they encounter the edge of a bac-
terial patch (Figure 1). To probe how ASI and ASK neural activity
could encode edge encounters, we analyzed their responses to
temporal changes in food stimuli. We found that ASK neurons
responded similarly to the removal after 1 or 5 min exposure
to bacterial stimuli (Figures 4F and S5F). Similarly, ASI re-
sponses to the addition of food were not affected by time inter-
val between two stimulations (Figures 4G and S5G). These
results indicate that ASI and ASK can reliably report stimuli
that are separated by at least a minute. Given the observed ac-
tivity patterns, we suggest that ASI and ASK neurons provide
information about the large fluctuations seen on the edge of a
food patch. AWC neurons, by contrast, provide signals that
do not influence patch-size-driven learning. Thus, ASI and
ASK neurons together detect variability in the food environment
A B
WT AWC- AWC::TeTx
ASI::TeTx
ASK::TeTx
eat-4
AWC ASI ASK ASI+ASK
% in
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se in
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% in
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C D
eat-4cDNA -
Food/odor
AS
K
AW
CA
SI
AnteriorPosterior
*** **
*%
incr
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in r
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dur
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loca
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glob
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earc
h
- AWC ASI ASK ASI+ASK
-20
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eat-4
********
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eat-4cDNA
***
-20
Figure 3. Specialized Roles of Sensory
Neurons
(A) Schematic showing three pairs of amphid
sensory neurons (ASI, ASK, and AWC) that extend
their dendrites to the nose of the animal and drive
local search behavior.
(B) Blocking neurotransmission by expressing
tetanus toxin light chain fragment (TeTx) in ASI or
ASK, but not in AWC, is sufficient to block patch-
size-driven plasticity in local search (n R 30) as
illustrated by percent increase in reorientations.
(C) eat-4 animals are unable to distinguish be-
tween patch sizes. Simultaneous rescue of eat-4 in
ASI and ASK is sufficient to restore patch-size-
driven plasticity (n R 24).
(D) Rescue of eat-4 in any of the three sensory
neurons is sufficient to rescue local to global
search transition, but not for worms to distinguish
between patch sizes. All bars are population
means, and error bars represent SEM (Kolmo-
gorov-Smirnov test, *p < 0.05, **p < 0.01, ***p <
10�3). In Figures 3, 5, 7, S6, and S7, search data
are presented as percent increase in the number of
reorientations when animals are removed from
large compared with those removed from small
patches.
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responding to large, but not small, changes in bacterial
concentrations.
Dopamine Acting via D1-like Receptors Modulates anInterneuron NetworkASI and ASK sensory neurons synapse onto a common set of
interneuron targets that includes AIY, AIA, AIB, and AIZ (White
et al., 1986) (Figure 5A). To test whether AIY, AIA, AIB, and
AIZ are involved in patch-size-dependent behavior, we blocked
neurotransmitter release from these neurons by expressing
tetanus toxin expression under cell-selective promoters
(Schiavo et al., 1992). We found that blocking neurotransmitter
release from any of the interneurons prevented patch-size-
dependent local search (Figure 5B, Table S1), suggesting that
the plasticity is a property of multiple interneurons and does
not occur at a single neuron or synapse.
To understand how the interneuron network is regulated, we
tested whether dopamine modifies behavioral plasticity in local
search. Dopamine has been shown to influence learning in
other model organisms (Dayan and Balleine, 2002; Hills
et al., 2004; Waddell, 2010; Wise, 2004). We found that
mutants in dopamine signaling do not show patch-size-
dependent plasticity (Figure 5C, Table S1). Specifically, we
found that worms with excessive dopamine, dat-1 dopamine
transporter mutants (Chase and Koelle, 2007), behaved simi-
larly to animals removed from small patches (Figure S6A),
while worms lacking dopamine, cat-2 tyrosine hydroxylase
mutants (Chase and Koelle, 2007), behaved similarly to ani-
mals removed from large patches, irrespective of prior food
experience. Given that animals lacking dopamine behave as
if they experienced a low variability, large patch environment
and perform a local search accordingly, we suggest that this
form of local search may be the default behavior. We further
hypothesized that increasing dopamine signaling would modify
local search behavior to reflect a small patch experience. To
test the hypothesis, we mapped the dopamine circuit and
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GASK ASI
Food Food Food
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LessFood
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More Food
Figure 4. Neural Responses to Varying Food
(A–C) AWC (A) and ASK (B) sensory neuron responses to removal and ASI (C) responses to the addition of bacterial stimuli.
(D and E) ASK (D) and ASI (E) respond to large, but not small, changes in bacterial stimuli.
(F) ASK shows similar response to removal of food stimulus after 1 or 5 min of food presentation.
(G) ASI neurons also respond similarly to the addition of food stimuli after its absence for 1 or 5min. Average data in each panel is shown using solid lines, while the
SEM is shown by the lightly colored region around each line. Bar graphs show the average change in fluorescence during the entire 50 s window after the addition
or removal of stimulus, shown for all conditions. Presence of stimulus is represented by gray boxes; * indicates significant difference between population means
(t test, p < 0.05, n R 10). Multiple comparisons were corrected for a false discovery rate of 0.05.
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analyzed the effects of manipulating dopamine levels on local
search behavior.
In order to identify the source of dopamine that influences
patch-size-dependent behavioral plasticity, we performed
cell-specific rescue experiments in dopamine mutants. In the
C. elegans hermaphrodite, dopamine is released from CEP,
ADE, and PDE neurons (Chase and Koelle, 2007). We were
able to restore patch-size-dependent search in cat-2 mutants
by expressing the wild-type gene specifically in CEP, but not
ADE and PDE dopaminergic neurons (Figure 5C, Table S1).
CEP neurons are postsynaptic to the ASK sensory neurons
(White et al., 1986), which we have shown above to be part
of the variability detection module. However, CEP neurons
also directly detect bacteria and drive locomotory behaviors
(Sawin et al., 2000). Therefore, we tested whether the sensory
function of CEP neurons is required for patch size-dependent
plasticity. We found that cat-6 mutants, which lack CEP sen-
sory cilia (Perkins et al., 1986), were still able to learn to distin-
guish between small and large patches, which suggests that
CEP sensory functions are not required for the plasticity in local
search (Figure S6B). Together, these results suggest that CEP
neurons, which receive synaptic connections from ASK, are
sufficient to release the dopamine required for local search
plasticity.
Next, we investigated the receptors that detect CEP-released
dopamine. TheC. elegans genome encodes several homologs of
the mammalian D1- or D2-like receptors (Chase and Koelle,
2007). We found that mutants in two D1-like receptors, dop-1
and dop-4, and the D2-like receptor, dop-3, were defective in
patch-size-dependent search (Figures 5D, 5E, and S6C, Table
S1). However, mutants in the D2-like receptors dop-2 and
dop-6 were able to distinguish the two patch sizes (Figure S6C,
B CA
AIZ AIB
AIAAIY
ASI ASK
CEP
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cat-2
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Figure 5. Interneuron Network for Learning Patch Size
(A) Schematic showing the proposed post-synaptic targets of the ASI and ASK sensory neurons required for patch-size-driven plasticity. CEP is a dopaminergic
neuron releasing dopamine (red circles).
(B) Expressing TeTx in any of the four interneurons blocks patch-size-driven plasticity (n R 34).
(C) Plasticity is also abolished in cat-2 and dat-1 mutants. cat-2 is specifically required in CEP neurons, but not in ADE or PDE neurons (n R 33).
(D) Mutants in the D1-like dopamine receptor, dop-1, are defective in patch-size-driven plasticity, and this receptor is specifically required in the ASI sensory
neurons (n R 30).
(E) dop-4 acts in AIB and AIZ, interneurons that are downstream of the ASI and ASK sensory neurons (nR 24). All bars represent populationmeans, and error bars
represent SEM; * indicates significantly different population means (Kolmogorov-Smirnov test, *p < 0.05, **p < 0.01, ***p < 10�3). Multiple comparisons were
corrected for a false discovery rate of 0.05.
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Table S1). Previous studies suggest that DOP-1 and DOP-3 play
mutually antagonistic roles, often in the same neurons. More-
over, mutants in these genes often display a phenotype that is
restored when both genes are defective (Chase et al., 2004).
Consistent with these results, we found that dop-1;dop-3 double
mutants were normal in their patch-size-driven local search (Fig-
ure S6C, Table S1). Thus, D1-like dopamine signaling is required
for patch-size-dependent plasticity. Using cell-specific rescues,
we found that DOP-1 receptors in ASI sensory neurons were suf-
ficient to restore plasticity (Figure 5D, Table S1). Similarly,
rescuing DOP-4 receptors in both AIB and AIZ interneurons,
which are postsynaptic to ASI and ASK sensory neurons (White
et al., 1986), is sufficient to restore plasticity (Figures 5A and 5E,
Table S1). To test whether dopamine was acting specifically in
the circuit driving plasticity of local search and not on dopamine
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Figure 6. Dopamine Levels Modify Plastic
Local Search and Neuronal Activity
(A) (i) Schematic showing the training and testing
phases of our learning assay. (ii) Addition of
dopamine changes in search strategy when added
during on-food experience, but not during search
(n R 30).
(B) Dose-dependent effect of dopamine added
during training suppresses turns during off-food
local search (n R 30). Significance is relative to
5 mM DA condition with multiple comparisons
corrected for a false discovery rate of 0.05. Buffer
is also significantly different from 5 mM DA and
10mMDA conditions. Bars in (A) and (B) represent
population means, and error bars represent SEM.
(C and D) Bar graphs showing calcium responses
of ASI and ASK neurons (n R 10) stimulated with
changes in food and averaged over 30 s after
stimulus change. Exogenous dopamine modifies
neuronal activity of ASI neurons (C), but not ASK
neurons (D). Bars represent population means,
and error bars represent SEM (Kolmogorov-Smir-
nov test, *p < 0.05, ***p < 10�3). Multiple compar-
isons were corrected for a false discovery rate
of 0.05.
receptors in other circuits, we examined
the dop-1, dop-2, dop-3, dop-4 (dop#)
quadruple mutant (Gaglia and Kenyon,
2009). The quadruple mutant was unable
to discriminate patch size, but local
search plasticity was restored when
DOP-4 receptor function was rescued in
AIB and AIZ interneurons alone (Fig-
ure S6D, Table S1). We suggest that while
dopamine signaling has widespread ef-
fects onC. elegans neural circuitry (Chase
and Koelle, 2007), dopamine receptors
are only needed on the ASI, AIB, and
AIZ neurons in the patch-size-dependent
behavior circuit. Collectively, our results
suggest that CEP neurons release dopa-
mine that can be sensed by D1-like re-
ceptors on ASI sensory neurons and the
downstream AIB and AIZ interneurons to regulate local search
plasticity.
Manipulating Dopamine Levels Modifies Plasticity andNeuronal ActivityTo further confirm a role for dopamine, we exogenously manipu-
lated its levels and analyzed its effects on patch-size-dependent
search behavior. We found that adding dopamine to the training
plate (see Experimental Procedures) during learning was suffi-
cient to modify subsequent local search behavior (Figure 6A,
TableS1). Bycontrast, addingdopamine to the local searchassay
test plate had no effect on this behavior (Figure 6A, Table S1).
Moreover, the dopamine effect was dose dependent, suggesting
that the dopamine present during on-food exploration plays a
crucial role in modulating local search behavior (Figure 6B). Our
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data show that increased dopamine suppresses the number of
turns during local search and suggests that dopamine levels are
instructive to the plasticity of local search behavior.
Next, using calcium imaging we tested whether dopamine
directly modifies the activity of target ASI neurons. We found
that ASI responses to the addition of large concentrations of
food were greatly increased in the presence of dopamine (Fig-
ure 6C). We also found that dopamine reduced ASI basal activity
upon stimulation with low food concentrations (Figure 6C), sug-
gesting that dopamine amplifies ASI responses to food. Consis-
tent with our genetic analysis of dopamine receptor mutants, we
found that dopamine did not modify ASK responses (Figure 6D),
indicating that dopamine acts on ASI, but not ASK neurons.
CREB Influences Acquisition TimeWe next tested whether patch-size-dependent search plasticity
requires protein synthesis and CREB signaling. Cycloheximide
has been used in C. elegans to block protein synthesis in a
dose-dependent manner (Kauffman et al., 2010; Szewczyk
et al., 2002). We designed a new behavioral assay in which we
transferred animals exploring a large patch to a small patch for
a 1 hr time period and then analyzed their local search after
removal from the small patch (Figure 7Ai). We predicted that
animals would learn the small patch size and switch their local
search behavior to reflect removal from a small lawn. Consistent
with our prediction, we found that wild-type animals switched
their behavior to small-patch-driven behavior (Figure 7Aii, Table
S1). We observed that blocking protein synthesis with cyclohex-
imide during small-patch exploration prevented learning the new
patch size; drug-treated animals behaved as if they had been
removed from the original large patch (Figure 7Aii, Table S1).
Moreover, cycloheximide did not have a general effect on
behavior. Drug-treated animals moved between similarly sized
patches did not exhibit changes in reorientations (Figure S7B,
Table S1). These data confirm that the animals require protein
synthesis during patch exploration to learn a new patch size.
CREB signaling is required for long-term memory in a number
of organisms including C. elegans (Kauffman et al., 2010; Silva
et al., 1998). Not surprisingly, we found that crh-1 (C. elegans
CREB homolog) mutants are unable to learn in our paradigm.
This defect was rescued when CREB function was specifically
restored to AIB and AIZ interneurons (Figure 7B, Table S1), sug-
gesting that CREB functions in AIB and AIZ interneurons to regu-
late learning of patch size. We hypothesized that changes in
CREB protein levels in interneurons might also influence local
search behavior. To test the hypothesis, we analyzed the
behavior of wild-type animals overexpressing crh-1 (CREB pro-
tein) in AIB and AIZ interneurons. We found that these transgenic
animals acted similarly to wild-type in their ability to learn patch
size and perform local search (Table S1). We modified the trans-
fer assay described above to test whether changing the level of
CREB protein in these AIB and AIZ interneurons would alter the
rate of learning patch size. In this modified assay, animals were
housed on large patches overnight and then transferred to small
patches for differing periods before we assessed their off-food
search behavior (Figure 7Ci). Consistent with the prediction of
the sensory filter (Figures 2A and 2C), we found that wild-type
animals took 30 min to learn the new environment and to switch
their behavior (Figures 7C and S7D, Table S1). We also observed
a correlation between the amount of CREB (increases in trans-
gene leads to increases in protein expressed; Mello et al.,
1991) and the time required to learn the small patch and switch
AB transfer off-food
i
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AIB/AIZ
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0
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+ CHX- CHX
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atch
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inut
es)
AIB/AIZ::crh-1(OE)
Figure 7. Molecular Mechanisms of Learning
(A) (i) Schematic of transfer assay showing animals moved from large to small patches with or without cycloheximide (CHX). (ii) Animals learned the distribution of
food on the small patch and switched their behavior, except when protein synthesis was inhibited by CHX; concentrations are indicated in each bar (n R 28).
(B) Learning requires crh-1 in the AIB and AIZ interneurons (n R 27).
(C) (i) Schematic of transfer assay indicates that (ii) animals overexpressing crh-1 in AIB and AIZ neurons learn more quickly than wild-type animals. Graph shows
length of time an animal must spend on a new, small patch before turning rate after removal is no longer statistically different from animals that explored small
patches overnight (Figure S7D). Learning rate is dependent on the amount of crh-1 transgene expressed in the interneurons. Animals lacking crh-1 do not show a
difference in turning rate between small and large patches. All bars represent population means, error bars represent SEM, and * indicates significance (Kol-
mogorov-Smirnov test, p < 0.05). Multiple comparisons corrected for using a false discovery rate of 0.05.
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behavior (Figures 7C and S7D, Table S1). Next, we tested
whether CREB acted downstream of the sensory neurons to
learn patch size by blocking neurotransmission from ASK. We
found that loss of ASK transmission slowed the rate of learning
compared to animals overexpressing crh-1 (Figure S7E, Table
S1), suggesting that this faster rate of learning also requires sen-
sory activity. Thus, we suggest that the amount of CREB protein
in AIB and AIZ interneurons regulates the time required to ac-
quire new information, providing a potential role for how CREB
may influence learning.
DISCUSSION
Our results show that C. elegans estimate environmental vari-
ability and use that information to alter local search accordingly.
These results are consistent with previous reports showing that
animals modify their locomotory patterns and egg laying based
on prior exposure to different foods (Shtonda and Avery, 2006;
Waggoner et al., 1998). Given our observation that local search
strategy used after removal from large patches (low variability
environments) is the default search behavior, we suggest that
C. elegans assume consistency in their food environment. This
likely reflects the statistics of signals in the natural environment
where low temporal and spatial frequencies dominate (Ruder-
man and Bialek, 1994; Simoncelli and Olshausen, 2001).
We found that when animals explore bacterial patches, ASI
and ASK neurons evaluate environmental variability. Encoding
environmental variability requires two signals: one representing
decrements in bacterial concentration, which is signaled
through ASK, and another representing increases in bacteria,
signaled through ASI. Our results suggest that low activity
in ASI and ASK neurons keeps this circuit in a default
configuration (typical large patch, low variability experience)
(Figure 8A). However, when ASK and ASI sensory neurons
receive inputs that indicate a high variability environment
(typical small patch experience), their activity is greatly
enhanced. We suggest that increased sensory activity
leads to increased dopamine release from the CEP neurons,
which are post-synaptic to ASK neurons. The increased dopa-
mine in turn acts on D1-like receptors on ASI sensory neurons
and the downstream AIB and AIZ interneurons in order to
modify search behavior in the new food-free environment
(Figure 8B).
Behavioral Model Predicts Future Local SearchBehaviorOur behavioral filter enabled us to predict certain characteristics
of future actions (frequency of reorientations) based on prior
experience. This was surprising, as previous studies have shown
that worm search behavior is stochastic (Gray et al., 2005;
Pierce-Shimomura et al., 1999). Previous attempts at behavioral
prediction in other animals have focused on estimating the prob-
ability of an animal performing a particular behavior (Coen et al.,
2014; Tai et al., 2012). By contrast, we were able to predict spe-
cific behaviors, such as the number of turns that an animal would
execute during a future local search, based on the history of their
prior experiences. Additionally, our prediction is for a behavior
that lasts many minutes after the initial stimulus (Figures 1F
and 2A–2C). Moreover, we show that dimensionality reduction
techniques designed for predicting properties of individual neu-
rons (Fitzgerald et al., 2011) can also be used to successfully pre-
dict whole animal behavior.
Dopamine and CREB Signaling Interpret SensoryInformationOur studies show that ASK and ASI sensory neurons detect vari-
ance in the food environment and modify local search behavior.
Combining these data with our behavioral filter, we suggest that
the variance detected between 5min and 25min prior to removal
from food is critical to generate local search. We also show that
dopamine released from the CEP dopaminergic neurons plays a
crucial role in modifying local search behavior. We suggest that
the sensory-neuron-detected envrionmental variability is stored
as the amount of dopamine in the circuit. Further, we speculate
that dopamine levels are integrated over the 25 min time interval
and interpreted by downstream neuronal circuitry to modify local
search behavior. Interestingly, dopamine levels are also inte-
grated over multiple timescales ranging from seconds to tens
of minutes to generate behaviors in vertebrate circuits (Schultz,
2007a, 2007b). Our results are consistent with those observed
in vertebrate models in which dopamine plays a crucial role in
motivation, reward, and risk-associated behaviors (Schultz,
BA Figure 8. A Sub-Circuit Nested within a
Larger Search Circuit Encodes Sensory
Variability to Generate Contextually Appro-
priate Local Search
(A and B) Schematic showing the learning circuit
when animals explore large (A) or small (B)
patches. On large patches, ASI and ASK neurons
are not active, and default behavior with more
turns is observed. On small patches, ASI and ASK
neurons respond to large fluctuation in bacterial
concentrations (seen at the edges), leading to
dopamine release from CEP neurons and fewer
turns. The dopamine is sensed by D1-like re-
ceptors on ASI and their post-synaptic partners
AIB and AIZ neurons. CREB acts parallel to
dopamine signaling in the same AIB and AIZ in-
terneurons. Arrows represent direction of infor-
mation flow from CEP neurons.
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2002; Schultz et al., 1997). In particular, increased dopamine has
been associated with unpredictable environments and an in-
crease in exploration, while reduced dopamine favors energy
conservation and less exploration (Beeler, 2012; Beeler et al.,
2012). These results confirm a conserved role for dopamine
signaling in regulating risk-associated behaviors from worms to
mammals.
Our results also suggest a novel role for CREB signaling, a
pathway that has previously been shown to play a crucial
role in regulating learning and memory (Frank and Greenberg,
1994; Silva et al., 1998). Different aspects of learning, such
as the value to be learned, learning rate, and timescale
of learning, have been shown to be under the control of
distinct neuromodulatory systems (Doya, 2002). We speculate
that learning during local search might also have indepen-
dent aspects, including acquisition value (information about
food variability) and acquisition rate (time needed to acquire
information about food variability. Dopamine signaling may
represent acquisition value, which is indicated as number
of turns during local search. While the amount of CREB pro-
tein influences the acquisition rate (time). We are unable to
find a direct link between dopamine and CREB signaling in
the local search circuit but find that exogenous dopamine
can modify the behavior of crh-1 mutants (Figure S7C). Taken
together, our results indicate that CREB and dopamine might
act in two parallel pathways to represent information in the
circuit.
Two Circuit Modules Estimate VariabilityWe suggest that two circuit modules within the local search
network combine to estimate variability over many minutes.
The first module consists of identified sensory neurons that
only respond to large changes in the sensory environment,
similar to ON and OFF cells in the retina (Wassle, 2004). Just
as ON and OFF cells act as edge detectors responding to large
increases or decreases in light intensity (Schiller et al., 1986),
ASI and ASK respond to large increases and decreases in bac-
terial concentration. These results suggest that diverse sensory
circuits use parallel ON and OFF pathways to evaluate vari-
ability as a general mechanism. The second module includes
interneurons, which are reminiscent of networks proposed for
reinforcement learning of average value (Schultz, 1997). This
is typically proposed as an error signal sent through dopamine
(Doya, 2002; Schultz, 1997; Schultz et al., 1997), similar to
the response of ASK, which could signal through CEP. This
suggests that the same circuit module may be used to learn
different types of value, such as average reward or reward
variability.
We propose that combining circuit modules may be
an evolutionarily efficient method for producing desired
behaviors. If the structure of neural circuits has evolved to
optimally extract relevant information, the local search circuits
described here and its computations are likely to be repre-
sented in other nervous systems. Further studies of nested
circuits should generate a better understanding of how neural
circuits estimate and use uncertain information to drive plastic
behaviors, crucial components of risk strategy and decision
making.
EXPERIMENTAL PROCEDURES
Please see Supplemental Experimental Procedures for details on strains,
molecular biology, calcium imaging, and hidden Markov model.
Imaging Bacteria Patches
Bacteria expressing GFP (Labrousse et al., 2000) were plated identically to
bacteria used for learning assays. Five plates, each containing patches of
10 ml, 50 ml, 100 ml, and 200 ml bacteria, were imaged on a Zeiss Stereo micro-
scope using a Zeiss MRM camera. As the highest fluorescence was at the
edge of the patch, peaks were fit to an ellipse in order to extract fluorescence
profiles and the bacterial center (Figure 1A). For each individual patch, a fluo-
rescence intensity profile was extracted every 36� and averaged. Normalized
profiles were found by dividing the distance from the edge by the radius of the
patch, so that the distance at the edge was 0 and at the center was 1 (Fig-
ure 1B). Profiles are well fit to a 1/R distribution from the edge to the center
of the patch. We designated the edge of the patch as the 20% of the patch
closest to where the bacterial lawn ends on a small patch and the remaining
80% the center. The edge of larger patches was chosen to represent the
same absolute distance from the transition between bacteria and no bacteria.
Behavioral Assay
Specific volumes (10 ml [small, 7.4 mm diameter], 50 ml, 100 ml [large, 19 mm
diameter], or 200 ml) of a bacterial culture (OD600 = 0.4) were seeded on
NGM agar plates (3g NaCl, 17 g agar, 2.5 g peptone, 1 ml of 1M CaCl2, 1 ml
of 5 mg/ml cholesterol, 1 ml of 1 M MgCl2, and 25 ml of 1 M KH2PO4 buffer
in 1 l of water) to obtain food patches of varying sizes. A dilution series was
used to estimate the number of colony-forming units in a bacterial culture
with OD600 = 0.4 and found it to be 5.18 3 108 cfu/ml. To characterize off-
food search behavior after exposure to different patch sizes, five to six L4
stage animals were first allowed to explore either small or large patches over-
night. Then, for off-food testing, worms were removed from these training
plates to assay plates lacking food, where they were corralled by a filter paper
soaked in 200 mM Cu(II)SO4 solution. Local search behavior on the assay
plates was recorded for 30 min using a Pixelink camera and analyzed by
custom software. Data presented were collected from at least six plates tested
on at least three different days.
Learning assays were performed similarly, except after overnight explora-
tion on large patches, animals were transferred to another plate containing
either a small or large patch of bacteria for 15, 30, 45, and 60 min (Figures
7C and S7D). For assays utilizing exogenous chemicals, 10 mM dopamine
or cycloheximide (0.1 mM–1 mM) was spread on agar plates and allowed to
absorb into NGM agar plates for 90 min. Either 10 or 100 ml of bacterial culture
was then seeded on these treated plates. The plates were allowed to dry for
60 min before animals were allowed to explore the patch for 1 hr. To obtain
dead bacteria patches, bacteria were killed in a water bath at 65�C for 3 hr
before plating. Diluted bacterial patches were obtained by diluting live or
dead bacteria cultures 10-fold in culture media. We obtained ‘‘bumpy’’ bacte-
rial patches by initially plating 1/10 of the volume of the culture onto a plate and
then adding the rest of the 9/10 onto that dried patch.
Prediction and Maximum Noise Entropy
In order to identify the on-food variables driving patch-size-dependent search,
we performed behavior assays as described above and analyzed resulting
data with a behavioral filter. Filters were extracted via the method of maximum
noise entropy (MNE) (Fitzgerald et al., 2011). Other methods such as reverse
correlation find the input that best correlates with the output. By contrast,
MNE finds the filter that maximizes uncertainty across trials in order to be
consistent with the known input-output relationships but makes no other as-
sumptions. Here, the parameters were binned into 3 min time periods to find
the time bins that best predict the behavioral output—the number of turns
upon removal from food. The prediction for the number of turns was obtained
by fitting a MNE feature as C½1+ expða+ f!, s!Þ��1, where C and a are con-
stants, the vector f!
represents time points forming the MNE feature, and vec-
tor s! represents a particular time profile describing the worm location at the
edge of the patch. The model makes different predictions for the number of
turns depending on the history of sensory experience described by s!. MNE
12 Neuron 86, 1–14, April 22, 2015 ª2015 Elsevier Inc.
Please cite this article in press as: Calhoun et al., Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans, Neuron(2015), http://dx.doi.org/10.1016/j.neuron.2015.03.026
filters were extracted using four subsets of the data (jack-knives), which were
then averaged. Final turning was predicted using the stimulus energy from the
MNE filter, and a subset of the data was then fitted with nonlinearity in accor-
dance with the linear-nonlinear model (Fitzgerald et al., 2011).
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
seven figures, and two tables and can be found with this article online at
http://dx.doi.org/10.1016/j.neuron.2015.03.026.
AUTHOR CONTRIBUTIONS
A.J.C. conceived and conducted the experiments, interpreted the data, and
co-wrote the paper. A.T. generated strains and conducted behavioral assays.
N.P. developed the software for tracking animals. J.A.J.F. took high-resolution
images of bacteria patches. T.O.S. interpreted the data, provided guidance for
generating the dimensionality reduction filter, and co-wrote the paper. S.H.C.
conceived the experiments, interpreted the data, and co-wrote the paper.
ACKNOWLEDGMENTS
We thank the Caenorhabditis Genetics Center (CGC), the National Brain
Research Project (NBRP, Japan), C. Bargmann, and R. Komuniecki for strains
and constructs; O. Hobert and N. Flames for cell-specific promoters; C. Ste-
vens for helpful discussions; and J. Simon for illustrations. We are also grateful
to L. Hale, B. Tong, C. Yeh, S. Leinwand, K. Quach, and H. Lau for helpful com-
ments on the manuscript and other members of the Chalasani laboratory for
critical help, advice, and insights. This work was funded by grants from The
G. Harold and Leila Y. Mathers Foundation to C. Bargmann and by The Rita
Allen Foundation and an NIH R01MH096881-03 to S.H.C. A.J.C was initially
supported by a graduate research fellowship from the NSF and later by a
training grant from NIMH. J.A.J.F. gratefully acknowledges financial support
from the Waitt Foundation, NCI P30 CA014195-40, and NINDS P30
NS072031-03. T.O.S. was supported by NIH Grant R01EY019493 and
P30EY019005 and National Science Foundation (NSF) CAREER Award
1254123.
Received: July 15, 2014
Revised: January 18, 2015
Accepted: February 20, 2015
Published: April 9, 2015
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