1-oct avoidance in C. elegans has been intensively studied because it is strongly modulated by nutrition state and monoamine/neuropeptide signaling cascades (34, 42–46). Our goal was to investigate the network basis of 1-oct repulsion using the NeuroPAL system (30), which allows simultaneous whole-brain Ca⁺⁺ imaging with unambiguous neuronal identification, coupled with quantitative analysis of 1-oct aversive behavior. 1-oct has previously been utilized as a volatile stimulus (33, 34), or in a gradient on agar plates (47, 48), while NeuroPAL recordings are performed in aqueous solution in microfluidics chips under confocal microscopy (30). To better match the conditions for NeuroPAL recordings and behavioral assays, we first investigated 1-oct repulsion in microfluidics arenas (49–51), which gave us precise control over the concentration and spatiotemporal characteristics of the 1-oct stimulus. Using a simple two-chamber arena (Fig. 1A), we demonstrated that worms were repelled by a saturating concentration of 1-oct in S-basal (2.2 mM, corresponding to a 3.75 x 10⁻⁴ dilution; Fig. 1B-D). In our initial trials, we added 0.01% w/v xylene cyanol (0.1 mg/mL) to the 1-oct solution to visualize the interface between the stimulus and control buffer. However, as another study reported (51), worms were significantly attracted to xylene cyanol, introducing an unwanted stimulus into the assays. We therefore optimized methods to maintain a stable interface between odorant and buffer chambers without the need for dye (see Methods). We assayed worms between 20 and 40 minutes after removal from food, during area-restricted search behavior (52), and calculated average chemotaxis indices (CIs) between 30 and 40 minutes (see Methods, and Fig. 1D, E). The −0.7 CI observed in the trial shown represents strong repulsion. The average CI over n=5 was −0.27±0.24 (SEM).

To investigate the neuronal basis for the observed 1-oct aversive behavior, we performed network-wide Ca⁺⁺ imaging during 1-oct stimulation and identified the responding cells by imaging the neuronal landmarks encoded by the NeuroPAL fluorescent reporters (Fig. 2A). Worms were immobilized in ‘olfactory’ microfluidics chips (53) for microscopy. Previous whole-brain studies have shown that certain neurons undergo spontaneous transitions between low and high activity states correlating with spontaneous reversals during foraging, notably the reverse command neurons AVA, AVE, AVD, AVB, and the RIM neuron (23, 27, 54–59). We therefore used a recording/stimulus protocol consisting of a 6 min pre-stimulus period, followed by 6 30s applications of 2.2 mM 1-oct, spaced 30s apart, to help distinguish between spontaneous transitions and potential sensory-evoked transitions within this neuronal population. We observed dynamic activity patterns in dozens of neurons before and during 1-oct stimulus (Fig. 2B-D). Consistent with previous studies, AVA, along with AVE, AVD, RIM, AIB, and AIZ showed spontaneous state transitions, with the AVB forward command interneuron and the AIY interneuron showing mirror image patterns (i.e. AVB/AIY inactivated when AVA activated and vice versa). Typically, 3-4 transitions were observed in a 12 minute recording. These transitions took place during both the pre-stimulus and stimulus periods (Fig. 2D, 3A) and showed no obvious relationship with 1-oct application or withdrawal. These transitions are related to network state changes which drive spontaneous reversals during foraging in freely moving worms (60). Immobilization and anesthetization, necessary for confocal imaging, distort certain aspects of these motor command sequences compared to freely moving worms executing the motor commands and receiving proprioceptive feedback. However, the intrinsic motor programs remain intact under these conditions (60).

Sensory signals were also prominent, in both amphid and non-amphid chemosensory neurons. ASH and BAG showed strong consistent responses correlating with stimulus onset (‘ON’ responses). AWB also showed an ON response but also had high levels of spontaneous activity. ADL showed a strong ON response to the initial stimulus but was strongly desensitized in subsequent applications (Fig. 2D and 3B). ASH, AWB, and ADL were previously identified as the main neurons mediating 1-oct aversive responses (33, 34). 1-oct responses were also observed in AWA, ASE, ASK, ASJ, and URX (Fig. 2D, 3B), consistent with the observation of Lin et al, showing that 1-oct activates sensory neurons promiscuously (39). Significantly, we observed a strong 1-oct response in AWC neurons (both AWC^OFF and AWC^ON; Fig 2D, 3B). AWC activity decreased upon 1-oct addition and rebounded upon 1-oct withdrawal, in a typical ‘OFF’ neuron pattern (61), opposite to ASH (Fig. 2B-D, 3C). This was surprising, first, because AWC is usually considered an attractive neuron, not expected to respond to a repulsive odorant, and second, because the observed AWC pattern predicts that AWC should drive attraction to 1-oct (38, 47, 61, 62). 1-oct has thus far only been described as a repellent for C. elegans, with no attractive properties (32, 34, 38, 48). Strong sensory-driven activity was also observed in AIA and SAA interneurons, suggesting these two neurons may receive substantial inputs from the sensory neurons (Fig. 2D, 3C). Apart from AIA, the other ‘first layer’ interneurons AIB, AIY, and AIZ did not show detectable sensory-driven activity (Fig. 3A), despite their extensive anatomical connections with sensory neurons, suggesting these interneurons may be receiving their principal functional inputs from elsewhere in the circuit.

To better understand how sensory inputs may pattern global network activity patterns, we performed correlation analysis on selected data sets in which we were able to identify 12 key neurons with well characterized roles in sensory motor coupling (38, 52, 61). To detect the sensory signature, we compared neuronal correlation patterns in the first 6 minute interval (prior to stimulation), and the second 6 minute interval (where stimulus was applied and removed for alternating 30s periods) (Fig. 3D, E). We observed the expected increases and decreases in correlation among the sensory neurons (i.e. ASH and BAG became correlated, and anti-correlated with AWC). We did not observe any increased correlation between the motor command interneurons and sensory neurons, consistent with a lack of observable sensory-driven activity in the motor command interneurons. However, the degree of activity cross-correlation among AVA, AVE, AVD, AIB, and RIM increased during the stimulus period, and AIB also showed increased correlation with ASH. These observations suggest that sensory information was penetrating to the locomotory command circuit level even if there was no obvious sensory entrainment of the principal neurons in those circuits (Fig. 3D, E).

Finally, we observed several neurons with activity patterns which did not correspond to the locomotory command group or the sensory-dependent group, which included SMDV, RIA, and RMDD (Fig. 2D), which are generally associated with turning behavior (63–65).

The observation that 1-oct application and withdrawal cycles activate ASH and AWC in alternation predicts that these two neurons generate antagonistic inputs into the locomotory circuitry. Specifically, ASH activity varies in phase with 1-oct application, predicted to stimulate in-phase reversal consistent with a repulsive chemotactic response. AWC activity varies out of phase with 1-oct application, predicted to inhibit in-phase reversals and stimulate out of phase reversals, consistent with an attractive chemotactic response. This puzzling activity pattern led us to hypothesize that 1-oct is encoded combinatorially, simultaneously as a repellant and an attractant through two distinct and opposing afferent pathways, with the balance of these pathways determining downstream events and eventual behavioral outcome.

To test this idea, we conducted several experiments to selectively inhibit the putative repulsive and attractive afferent pathways singly, with the goal of unmasking the effects of the opposite pathway. First, to selectively diminish the repulsive pathway, we reduced the 1-oct concentration. Lin et. al. reported 1-oct concentration-response relationships for the amphid sensory neurons including ASH and other neurons implicated in 1-oct avoidance (ADL, AWB), and AWC (39). At sub-saturating concentrations, the responses of ASH, and to a lesser extend AWB and ADL, showed clear concentration dependence, while the ‘ON’ response of AWC did not. We extended these observations to the concentration ranges we used in our behavior and imaging experiments, measuring ASH responses, and both the AWC ‘ON’ and ‘OFF’ responses, during serial stimulation of individual worms (0.22 mM, 0.66 mM, and 2.2 mM 1-oct). ASH responses showed significant concentration-dependent increases, whereas AWC ‘ON’ and ‘OFF’ responses did not (Fig. 4A-C). In parallel, we found that lower concentrations of 1-oct (0.22 and 0.66 mM) were strongly attractive (Fig. 4D), while intermediate concentrations were largely without effect (Fig. 4D). Thus, the 1-oct chemotactic response transitions from attraction, through neutral, to avoidance, as the activity of ASH (and possibly other neurons) increases relative to AWC, highlighting the potential importance of the balance of repulsive and attractive input pathways (note that 1-oct repulsion at 2.2 mM in wild type was modest and highly variable; the solubility limit of 1-oct in aqueous solution prevented testing higher concentrations). Next, we took a genetic approach using tax-4 and osm-9 mutations to selectively inhibit different sets of sensory neurons. TAX-4 is a cGMP-gated ion channel required for signaling in AWC and other neurons (including AWB, BAG, ASE); OSM-9 is a TRP channel which contributes to signaling in ASH and other neurons (including ADL, AWA) (38, 44, 66). Loss of TAX-4 caused 1-oct to become repulsive at all concentrations (Fig. 4E), consistent with an obligatory role for TAX-4 in the putative attractive pathway. Surprisingly, loss of OSM-9 had no significant effect on CI values at any concentration (Fig. 4E), suggesting that these mutants may retain some residual ASH signaling (38, 67) or 1-oct repulsion may be due to multiple sensory neurons acting redundantly, some of which do not signal through OSM-9. Finally, we directly tested whether ASH and AWC, specifically, play antagonistic roles in 1-oct chemotaxis. We used chemogenetic inactivation, based on transgenic expression of Drosophila HisCl1 in ASH (using the sra-6 promoter) and AWC (using the ceh-36 promoter) (68, 69). Incubating these strains on histamine (His) results in acute, highly specific neuronal inactivation. Using 2.2 mM 1-oct as stimulus, His-minus controls were not significantly different from wild type for either the ASH-HisCl or the AWC-HisCl strain (despite the appearance of slight attraction under these specific experimental conditions). Importantly, the addition of His significantly skewed chemotaxis indices in the expected direction: CI values shifted positively for ASH HisCl1 worms (Fig. 4F) and negatively for AWC HisCl1 worms (Fig. 4G), consistent with reduced repulsive drive and reduced attractive drive, respectively. These findings strongly support the hypothesized antagonism between ASH and AWC in 1-oct chemotaxis. Overall, these three approaches provided evidence that 1-oct chemotaxis is driven by two distinct afferent input pathways with opposite valence in balance with one another, which can be distinguished by their differential concentration dependence, their differential usage of signal transduction pathways, and different primary sensory neurons providing the initial input (i.e. ASH for the repulsive pathway and AWC for the attractive pathway).

To understand the interaction of these co-activated afferent pathways at the level of locomotory decision making, we closely examined the behavior of worms as they encountered the interface between 1-oct solution and control buffer in microfluidic devices. We performed this analysis using the microfluidics ‘stripe’ chip (50, 51), which contains a central zone of 2.2 mM 1-oct flanked by zones containing control buffer. We used automated tracking (70) to quantify behavior upon encountering zone boundaries (i.e. reversals vs successful transits; Fig. 5A, B). For worms entering the 1-oct zone, entry transits outnumbered entry reversals by nearly 2:1 (Fig. 5B, C), which was surprising given that the overall behavioral outcome is generally avoidance under these conditions. osm-9 mutation further depressed entry reversals, indicating that osm-9 dependent signaling contributes to reversal in response to 1-oct in the microfluidic environment. Interestingly, tax-4 loss-of-function strongly stimulated entry reversals, indicating that tax-4 signaling normally suppresses these reversals, consistent with a role promoting 1-oct attraction (Fig. 5B, C). This attraction-promoting activity was also observed for worms exiting the 1-oct zone (Fig. 5B, C). Here, exit reversals strongly outnumber exit transits but in tax-4, the pattern is reversed, with transits outnumbering reversals by roughly 4:1. osm-9 loss of function had no effect on exit reversals (Fig. 5C). These observations confirm that TAX-4 mediated attractive signaling significantly shapes the behavioral response to 1-oct, even though 1-oct is traditionally viewed as a strong repellent. Most importantly, they identify a locomotory transition important for chemotaxis (i.e. entry reversal frequency) in which two antagonistic sensory afferent pathways converge at a single point in time and space (Fig. 5B).

These findings were largely recapitulated by the chemogenetic inactivation of ASH and AWC. We analyzed reversal behavior at the 1-oct/buffer interface in the HisCl1 experiments shown in Fig. 4E and 4F (performed in the smaller two-chamber microfluidics arenas) (71). Inactivation of ASH had no significant effect on locomotory decision-making. However, these datasets contained very few entry reversals to begin with, so we were unable to assess the role of ASH in these events. Exit reversals were also unaffected, similar to osm-9 trials. Importantly however, inactivation of AWC produced a very similar result to tax-4 mutation. Entry reversals were significantly increased and exit reversals were strongly suppressed, pointing to a key role for AWC specifically in the attractive sensory afferent pathway for 1-oct chemotaxis.

These behavioral data raised a new important question: how can wild-type worms avoid the 1-oct containing zone when their reversal decisions favor entry and retention? We had noticed that contact with 2.2 mM 1-oct caused worms to rapidly and reversibly increase their locomotory activity, suggesting an additional potential mechanism in play. To quantify this effect, we measured the average distance worms moved when we flooded the microfluidics arena with 2.2 mM 1-oct, comparing the distances traveled in a 2-minute interval in control buffer and 1-oct. Worms significantly increased their locomotion in 1-oct, and returned to baseline rates 2-4 minutes after 1-oct withdrawal (Fig. 5E). Next, we created a simple behavioral simulation to determine whether this selective increase in locomotory activity could counteract the observed bias in reversal probabilities to achieve overall avoidance. The environment was modeled as a line divided into two equal compartments, representing the control buffer and the 2.2 mM 1-oct halves of the arena. Worms were modeled as one-dimensional particles, free to move along the full length of the line. They were allowed to reverse direction at random along the length of the line but were specified to reverse direction with fixed probabilities at the buffer/octanol interface, depending on which direction they approached from. Finally, the particles moved at different relative rates in the buffer and 1-oct compartments (Fig. 5F). Using our measured entry and exit reversal probabilities as inputs, this simulation showed that relative locomotory speed is a critical factor determining overall chemotaxis behavior, with a 60% speed increase sufficient to convert attraction to repulsion, given our observed interface reversal probabilities in the stripe chips (Fig. 5F). This simulation was highly simplified and did not account for the persistence of higher locomotion speeds once worms exited 1-oct (Fig. 5E), which could further potentiate repulsion since faster-moving exiting worms will be oriented away from the 1-oct zone, biasing their trajectories toward the further reaches of the buffer zone where they eventually slow. Importantly, these results extend the idea that 1-oct is encoded combinatorially, generating afferent inputs controlling at least three distinct aspects of locomotion: 1) promotion of reversals when encountering increased [1-oct], 2) promotion of reversals when encountering reduced [1-oct], and 3) increase of locomotory speed in the presence of increased [1-oct].

To understand how the repulsive and attractive 1-oct signals affect locomotory reversals at a circuit level, we examined the activity pattern of the AVA reverse command interneuron in relation to 1-oct addition and withdrawal. First, it was necessary to develop a data analysis approach to detect and quantify sensory-driven patterns of AVA activation, given the well-documented patterns of spontaneous AVA activity state transitions (23, 27, 54–59) that could potentially obscure a sensory signal. To reduce the impact of these transitions, we averaged at least 6 recordings of AVA activity for each condition tested so that non sensory-associated AVA transitions would tend to average out with larger numbers of recordings, akin to spike-triggered averaging (72–74). We then calculated the ‘entrainment index’ (EI), as a measure of how strongly a response is positively or negatively correlated with 1-oct application, and quantified the probability that an observed correlation is greater than a random association (see Methods), setting a threshold of P<0.05 for non-random association. To validate this test, we analyzed ASH and AWC responses to 1-oct application, which were obviously highly correlated and anti-correlated, respectively. The ASH and AWC EIs showed the expected positive and negative values, with P <0.0015, indicating a less than one in six hundred chance that the correlations in neuronal responses with 1-oct applications were random (Fig. 6A, B). Applying this test to AVA activity, we observed strong inverse correlation with stimulant application at 0.22 mM 1-oct (the lowest tested concentration), and a significant negative EI value (Fig. 6C, D, H). This concentration was attractive and showed the lowest ASH:AWC activity ratio (Fig. 4C), suggesting that AWC may be driving AVA with minimal interference from ASH. At higher [1-oct] (0.66, 2.2 mM), AVA entrainment with 1-oct application was shifted positively (compared to 0.22 mM; Fig. 6C, D, H) and was not significantly different from random (P>0.05). At these concentrations, the ASH:AWC activity ratio is higher (Fig. 4C), consistent with the idea that a lack of entrainment may reflect interference between AWC and ASH signals impinging on AVA.

To further support the idea that conflicting sensory signals may mask one another at the level of AVA, we measured EIs in TAX-4 animals. At the lowest [1-oct], the strong negative entrainment previously observed was abolished, confirming that negative entrainment is a function of TAX-4 signaling (Fig. 6E, F, H). At the intermediate [1-oct] (0.66 mM), tax-4 loss of function mutation caused a slight positive shift in the EI, but did not lift it above the threshold for significant entrainment (P>0.05) (Fig. 6E, F, H). However, at the highest [1-oct], loss of TAX-4 positively shifted the EI into the significant range for positive entrainment (Fig. 6E, F, H). Interestingly, all of the calculated AVA EIs (Fig. 6H) correlate well with the overall chemotactic behavior in terms of sign (i.e negative entrainment predicts attraction, positive entrainment predicts repulsion), even where the strength of entrainment with the sensory input did not exceed our set threshold for non-random entrainment (see Discussion). Most importantly, these results imply that the reverse command interneurons under these assay conditions will not necessarily show overt entrainment to sensory stimuli even though they are receiving sensory inputs because sensory stimuli may be encoded as parallel antagonistic afferent inputs which cancel one another out, until the prevailing conditions permit one input to become substantially stronger than the other.

To investigate how the 1-oct sensory signals are transmitted to AVA, we examined the interneuron AIB, which is positioned to be an integrating hub in this system because it receives afferent inputs from ASH, AWC, and many other sensory neurons, and from RIM, which provides feedback about the motor state that is used in the regulation of sensory afferent pathways by corollary discharge (54, 75, 76). Initially, we did not observe significant entrainment of AIB activity with 2.2 mM 1-oct application (Fig. 6G). It is possible that immobilization and anesthetization may be affecting AIB responses to sensory activity and/or proprioceptive feedback from locomotion. However, it is also possible that motor feedback from RIM was obscuring the sensory signal. To address this possibility, we subtracted AVA activity, representing the motor state, from the AIB activity (AVA closely mirrors RIM), based on the observation that AIB activity can be modeled as the sum of convolutions of motor activity and sensory activity (77). This modified AIB signal showed significant entrainment with 2.2 mM 1-oct application which was positive in sign (Fig. 6 G and H), and greater than AVA’s entrainment at the same 1-oct concentration. These observations suggests that AIB serves as a sensory relay for the aversive inputs, but since aversive entrainment in AVA is weaker than that in AIB (presumably due to interference from attractive inputs), the site of integration between repulsive and attractive pathways must be downstream of AIB and either at or upstream of AVA.

What benefit might C. elegans gain from encoding 1-oct combinatorially, simultaneously activating attractive and repulsive afferent input pathways? This strategy could create a simple and versatile mechanism to modulate the behavioral output to best match internal and external conditions, such as specific odorant concentration, the presence of other odorants, or interoceptive inputs such as hunger or stress. A combinatorial strategy allows response sensitivity and even valence to be altered by simply adjusting gains or synaptic outputs within each pathway (Fig. 7A, B), without requiring extensive functional reorganization of the downstream connections, or recruitment of complex cross-inhibitory circuitry. For example, in the laboratory, 1-oct is used as a repellant for C. elegans, but its presence in the environment may convey more complex information. 1-oct is toxic to worms (thus representing a risk), which presumably explains why C. elegans has evolved to avoid it, at least at higher concentrations. However, 1-oct is naturally found in decaying plant material (78, 79), so it may also signal the presence of a bacterial food source for worms (thus representing a reward as well). In the presence of non-toxic alternatives, other food sources are safer, but in the absence of alternatives, worms may risk toxicity to avoid starvation. Could combinatorial coding support the risk-reward decision making required of C. elegans when evaluating environmental 1-oct signals? Consistent with this idea, many attractive food signals, including isoamyl alcohol (IAA), suppress AWC activity (35, 39, 61), and could therefore decrease the activity of the attractive afferent pathway to disinhibit repulsion (Fig. 7A, B). We tested this hypothesis by conducting chemotaxis experiments at 0.66 mM 1-oct, normally an attractive concentration in our microfluidics assays, in the presence of isoamyl alcohol (IAA, 10⁻⁴ dilution, added to both the 1-oct and control buffer sides of the microfluidics arena). Under these conditions, worms were strongly repelled by 1-oct (Fig. 7C). The probability of reversals when entering the 1-oct zone was increased (Fig. 7D), the probability of reversals when exiting the 1-oct zone was decreased (Fig. 7D), and the repulsion-inducing modulation of locomotory speed was also increased (Fig. 7E, F). Interestingly 0.66 mM 1-oct did not cause a speed increase on its own (unlike 2.2 mM, Fig. 5). Acute application of IAA also did not affect speed. However, 0.66 mM 1-oct in the presence of IAA caused a large speed increase (2-fold). Since IAA reduces AWC activity, this IAA effect is consistent with removal of AWC inhibition of 0.66 mM 1-oct-dependent speed modulation. These three quantitative changes in locomotory parameters will each shift the chemotaxis index in the negative direction, converting 1-oct attraction to repulsion.

In network recordings, the presence of IAA greatly increased the sensory entrainment of AVA, positively correlating with stimulus application. This effect suggests that IAA will potentiate 1-oct avoidance by klinokinesis, or in other words, increasing the probability that worms will execute a reversal when they experience positive d[1-oct]/dt. These results provide proof-of-principle that rebalancing the afferent input pathways to reduce attraction and promote repulsion can control chemotaxis in an ethologically relevant manner and demonstrate the potential utility and flexibility of the combinatorial coding strategy.

C. elegans were maintained at 20°C on NGM agar plates and fed E. coli OP50 bacteria. All experiments were conducted at 20-22°C (93, 94). The following strains were utilized:

N2: Wild-type.

All behavioral studies were conducted in microfluidic devices following standard protocols (49–51). Devices were fabricated by thoroughly mixing polydimethylsiloxane (PDMS) and a curing agent in a 10:1 ratio by weight (Sylgard 184, Electron Microscopy Sciences Cat. 24236 – 10), degassing the mixture under vacuum, and then pouring it onto a silicon mold at ̴̴ 5 mm height. PDMS was cured by incubating at 65°C for at least 3 hours. Olfactory chips were obtained from Microkosmos (ukosmos.com).

Neuronal activity was examined by measuring calcium transients detected by fluorescence change (GCaMP6s, OH15500 (30), AML320 (96)). Calcium imaging was conducted as previously described (23, 53, 54). Animals were anesthetized with 1 mM tetramisole hydrochloride (Sigma, CAS 16595-80-5) in S-basal buffer and imaged in PDMS olfactory microfluidic devices, which trap the worm in a chemical delivery system and provides inlets to deliver either buffer or stimulus to the nose using laminar flow (53). Head-ganglia neurons were recorded using Leica confocal microscope (SP8) at 0.6 - 0.8 Hz rate. Odorants were diluted in S-basal. Each worm was recorded for 12-14 minutes within 20-40 minutes from starvation onset, starting with 6 minutes of buffer exposure followed by 6-7 30-second pulses of stimulus with 30-second intervals. Calcium imaging was followed by immediate recording of NeuroPAL color markers. For +IAA experiments, IAA delivery to the nose was started after 5 minutes of recording (1 minute before the first stimulus application) and IAA was was present in the stimulus and buffer channels thereafter. For concentration-response of ASH and AWC experiments, three 0.22 mM, two 0.66 mM and two 2.2 mM 1-oct pulses were given serially to the same worm; repeated application produced identical response amplitudes.

Recorded image files were stacked and analyzed using Fiji/ImageJ (91) followed by motion correction with ‘MoCo’ plug-in (92). Neuron identity was manually assigned and matched with calcium response using guidelines described by Yemini and Hobert (30). Neurons with ambiguous identities were excluded from the analysis (Fig. 2). Individual neuron activity was then normalized and re-sampled, before averaging, by ‘Min-Max normalization’ and ‘Linear interpolation’ respectively, using custom R-scripts (97).

Statistical analyses were performed using Prism v10.2.1 (GraphPad Software Inc., La Jolla, CA), R v4.3, R-Studio v2022.07.1 (Posit PBC, Boston, MA), MatLab vR2023A (MathWorks, CA) and Microsoft Excel 2016. Chemotaxis assays were repeated 3-5 times and analyzed by Student’s t-test (un-paired). Pooled data from distance-traveled assays were analyzed by one-way ANOVA with post-tests. Reversal assays were repeated 3 times, data were pooled, and analyzed using Fisher exact tests.

Entrainment indices (EIs) were calculated using averaged, normalized calcium responses. First, area-under-curve (AUC) values were calculated for all possible 30s epochs by summing the Ca⁺⁺ signal intensities over 30s after first subtracting the corresponding initial Ca⁺⁺ signal intensity. EIs were calculated by adding six AUCs; stimulus-dependent EIs were calculated by adding the six AUCs corresponding to stimulus application intervals, while random EIs were calculated by adding six randomly-chosen AUCs. The significance of stimulus entrainment was estimated as the probability that the stimulus-dependent EI would exceed any given random EI, with probabilities < 0.05 considered significant (i.e. P<0.05). A P value <0.0015 indicated that the experimental EI was larger (or smaller) than all possible random EIs within the dataset. EIs for AVA were calculated using a 10-second lag, across all conditions, because, where obvious sensory entrainment was seen, AVA signals showed consistently slowed responses compared to sensory signals. No lag was incorporated for sensory and first-layer interneurons (ASH, AWC, AIB).

Neuronal activity correlations were calculated using Spearman’s rank correlation test and corrected with Fisher’s z-transformation before testing statistical significance (98–100), using custom R scripts.