To test our first assumption, namely that P-EN neurons mirror-symmetrically encode angular velocity in the right versus left side of the protocerebral bridge, we expressed GCaMP6f in P-EN neurons (under the control of the R37F06-GAL4 driver) and imaged the calcium activity of the left and right P-EN subpopulations in the noduli, a third, paired structure that is innervated by these neurons (Wolff et al., 2015, Figure 2A). All P-EN neurons whose dendrites project to glomeruli on the right side of the bridge innervate the left nodulus, whereas those on the left side of the bridge project to the right nodulus. The proximity and compact nature of the noduli enables unambiguous assignment of activity to the left and right P-EN populations with a high signal-to-noise ratio (Figure 2B,C).

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P-EN population calcium activity in the noduli of flies walking in the dark was strongly modulated by the fly’s angular velocity. When the fly turned clockwise, P-EN activity increased in the left nodulus, and vice versa. This led to an apparent flip-flopping of activity between the two noduli as the fly made turns in either direction (Figure 2C). Thus, the difference between calcium transients evoked in the two noduli was correlated to the fly’s angular velocity (Pearson’s R = 0.65 ± 0.14, mean ± SD, N = 10 flies, p<0.001 in all cases, Figure 2C,D, see Materials and methods). In fact, noduli calcium responses were dominated by the fly’s angular velocity and largely indifferent to the fly’s forward speed, as revealed by generalized linear regression analysis (Figure 2E, see Materials and methods). Thus, P-EN population activity is mirror-symmetrically tuned to angular velocity, with neurons innervating the right side of the bridge (and therefore the left nodulus) preferentially responding to clockwise rotation and vice versa, matching the requirements of our conceptual model (Figures 1H and 2A).

To understand how individual P-EN neurons respond as the fly turns, we performed somatic loose patch and whole cell patch clamp recordings from identified neurons in tethered walking flies expressing GFP under control of the same GAL4 driver as used for calcium imaging, R37F06 (Figure 3A). We filled cells with Alexa dyes and confirmed neuron identity and soma location by epifluorescence imaging and/or post-hoc confocal imaging of PFA-fixated brains (Figure 3A, see Materials and methods). We let head-fixed flies walk in darkness during the recording, or, alternatively, linked the rotational component of their movements on the ball to the angular position of a 15° wide vertical stripe (visual closed loop, see Materials and methods).

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As hinted at by P-EN population imaging in the noduli, the electrical activity of individual P-EN neurons was strongly modulated by rotational velocity. We found that P-ENs segregated into two anatomically defined subpopulations: Neurons with their soma in the right hemisphere increased their activity during turns to the right and decreased it during turns to the left, whereas neurons residing in the left hemisphere showed the inverse activity profile (Figure 3B,C and Figure 4A,B). Note that P-EN responses to changes in rotational velocity were similar in darkness and visual closed loop conditions (see Figure 4—figure supplement 1A,D,E,H), which allowed us to pool data for all population statistics presented in Figure 4. Consistent with our previous observations, P-ENs were generally not tuned to forward velocity (see Materials and methods and also Figure 4—figure supplement 1B). We next quantitatively characterized P-EN responses during the fly’s turns by computing rotational velocity tuning curves. For each cell, we sorted rotational velocities of all walking periods into 12°/s bins and fit the mean P-EN spike rate across bins with a weighted sigmoidal function (Figure 4A; mean R² = 0.87 ± 0.01, N = 12, see also Figure 4—figure supplement 1C; see Materials and methods for details). On average, P-EN spike rates increased by 5.6 Hz during fast turns in the preferred compared to fast turns in the non-preferred direction, but there was considerable heterogeneity across cells (‘rate modulation’: 5.6 ± 3.7 Hz, N = 12; Figure 4B and Figure 4—figure supplement 1D). Likewise, P-ENs were heterogeneous in the range of rotational velocities over which their activity was modulated (‘P-EN bandwidth’: 145 ± 82°/s, N = 12). Overall, however, P-EN bandwidth corresponded well with the range of rotational velocities displayed by turning flies (Figure 4—figure supplement 1E). We also noted that the P-EN bandwidths of the left and right P-EN subpopulations were largely overlapping (Figure 4C, left), so that the inflexion points of their tuning curves (the rotational velocities for half-maximal P-EN activation) were clustered around 0°/s (Figure 4C, right). Thus, when the fly is walking, the total activity of the P-EN population is roughly constant, with the contributions of the left and right P-EN subpopulations depending on the fly’s momentary rotational velocity (Figure 4—figure supplement 1F).

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To examine whether increases in P-EN activity preceded upcoming turns, we computed spike-triggered averages (STAs) of instantaneous rotational velocity during periods of significant turning (with instantaneous rotational velocities greater than 40°/s, see Materials and methods). Across all P-ENs, we found that spikes occurred well after the peak of the rotational velocity, on average by 123 ± 43 ms (N = 12) (Figure 4D). In an alternative approach, we used generalized linear regression to fit the instantaneous P-EN spike rate with the past, present, or future rotational velocities by introducing a series of lags between the two parameters (Figure 4—figure supplement 1G, see Materials and methods). In all P-ENs, the maximum difference between left and right turn correlation coefficients occurred at positive temporal lags (130 ± 73 ms, N = 12), confirming that P-EN spiking activity changed as a response to turns instead of preceding them (Figure 4—figure supplement 1H). Overall, individual P-ENs encode the fly’s recent rotational velocity, with left and right P-ENs segregating into two subpopulations with mirror-symmetric tuning profiles.

Since our conceptual model relies on the P-ENs to also be tuned to the fly’s heading (Figure 1H), we next asked if the patch-clamp recordings would reveal such properties. To avoid complications caused by error accumulation in flies walking in the dark (Seelig and Jayaraman, 2015), which would result in a drift of the neuron’s preferred heading angle over time, we first restricted this analysis to experiments with closed loop visual feedback (see Materials and methods). We constructed two-dimensional tuning maps of P-EN spike rates to rotational velocity and virtual heading, which revealed localized peaks in firing rate (Figure 5A). We collapsed these maps along either of the two axes to fit the P-EN’s tuning to the fly’s virtual heading with a compound von Mises function and its tuning to rotational velocity with a sigmoidal function, as before (Figure 5A, see Materials and methods). For each cell, we also calculated indices for rotation and heading tuning (Figure 5B, see Materials and methods), which were, in all but one case (p=0.0676), larger than the 95^th percentile of the shuffled control distribution.

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P-EN heading tuning was generally stable over time, although we occasionally observed drifts in the neuron’s preferred heading angle (Figure 5—figure supplement 1A). We assessed tuning stability more quantitatively by analyzing short epochs (5–10 min) that sampled a sufficient range of rotational velocities (≥250°/s) across all virtual heading angles (see Materials and methods). We identified seven such epochs in six different cells recorded in flies walking with closed loop visual feedback as well as in darkness (see Figure 5C,D for an example and Figure 5—figure supplement 1C for tuning indices, which were, for all but one epoch, larger than the 95^th percentile of the shuffled control distributions). For all epochs recorded in flies walking with visual feedback, we found that the P-ENs’ preferred heading angles during the epochs differed by only 6 ± 5% from the average of the entire experiment (population mean, N = 4). As a second line of evidence, we found the P-EN bump width to be roughly similar for short epochs and across whole experiments, suggesting that a neuron’s preferred heading angle did not drift much over time (average tuning curve width at half-maximal P-EN heading modulation: 56 ± 20° (N = 7 epochs) versus 76 ± 35° [N = 5 experiment averages]). Overall, we found P-EN neurons to be tuned to the fly’s heading as well as the fly’s rotations, both when flies walked under visual closed loop conditions and in darkness (Figure 5—figure supplement 1C).

Having established that P-EN neurons are conjunctively tuned to the fly’s heading and rotations, we examined our recordings for hints of integrative computations within individual neurons. Specifically, we began by asking whether P-EN activity was tuned to one of the two parameters even in the absence of contribution from the other. Figure 6A–B depicts an example of strong P-EN tuning to rotational velocity as well as to virtual heading for a fly walking in darkness. To quantitatively assess whether tuning to one parameter depended on the other, we computed the rotation and heading tuning indices separately for the respective preferred and non-preferred conditions, i.e. one rotation index each for the fly’s turns within the preferred versus non-preferred heading quadrant and one heading index each for when the fly rotated in its preferred versus non-preferred turn direction. These tuning indices were not significantly different (Figure 6C, left). Along the same lines, the preferred heading angle of individual P-ENs (the mean vector angle of spike rates across headings) was similar for preferred and non-preferred rotations (Figure 6C, bottom right). Following the same logic, we also fit conditional tuning curves to subsets of the parameter space (see Materials and methods for details). As evident from the example (Figure 6D) and the population data (Figure 6E), strong conjunctive tuning was apparent in the pronounced differences in rate modulation between preferred and non-preferred conditions. Nevertheless, we found P-EN spiking to be tuned to rotational velocity in non-preferred heading quadrants and to heading during non-preferred rotations (Figure 6E left; rotation tuning at non-preferred heading angles: 2.3 ± 2.1 Hz, heading tuning during non-preferred rotations: 1.2 ± 1.1 Hz; one sample t-test: p=0.026 and p=0.043, respectively; N = 6 for both comparisons). The pronounced conjunctive tuning we observed at the level of spike rates was less apparent in the subthreshold activity of P-EN neurons (Figure 6—figure supplement 1). In contrast to the spike rates, for which rate modulation in response to one parameter strongly depended on the other, quantitative modulation of the membrane potential did not show significant mutual dependence between the two parameters (Figure 6—figure supplement 1C, see Materials and methods for details). These observations suggest not only that tuning of P-EN output is strongly conjunctive, but also that this property may be sharpened within single P-ENs.

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We next sought to test the third pillar of our conceptual model, that the E-PG and P-EN neurons are connected in an excitatory loop. Light level analysis of P-EN neuron morphology suggests that they likely have post-synaptic specializations in the bridge and pre-synaptic boutons in the ellipsoid body, while E-PG morphology indicates the opposite polarity (Wolff et al., 2015), making it possible for the two cell types to be mutually connected. We sought histochemical confirmation of this polarity by expressing the presynaptic reporter synaptotagmin-smGFP-HA (Aso et al., 2014) in GAL4 lines that drive expression in P-EN and E-PG neurons, respectively. Light level analysis of brains in which HA-tagged synaptotagmin was expressed in P-EN neurons revealed stronger labeling in the ellipsoid body and noduli than in the bridge (Figure 7A, 11/14 brains). In contrast, analysis of the E-PG targeted brains revealed high levels of HA-tagged synaptotagmin in the protocerebral bridge, but also in the ellipsoid body (at higher levels than in the ellipsoid body in 5/15 brains, at comparable levels in both neuropils in 10/15 brains, see Figure 7B). These results are consistent with the anatomical finding that the P-EN neurons receive input in the bridge and send output to the ellipsoid body.

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To confirm these key connections, we then performed functional connectivity experiments between the two neuronal subtypes in ex vivo preparations. We expressed the red-shifted channelrhodopsin CsChrimson (Klapoetke et al., 2014) in either the E-PG or the P-EN populations while expressing GCaMP6m or GCaMP6f in the putative downstream partner cell type. Optogenetic activation of P-EN neurons reliably evoked positive calcium transients in E-PG neurons (Figure 7C, see Materials and methods), suggesting that angular velocity information is indeed relayed to E-PGs via this pathway. However, activation of the E-PG population induced more variable responses in P-ENs. Although we observed responses consistent with excitatory connections when we used strong light intensities for optogenetic activation (Figure 7D), the same pairing evoked activation, inhibition, and a lack of response at different times with weaker light intensities (dotted line in Figure 7D, inset). Thus, the excitatory loop between E-PG and P-EN neurons may be both direct and indirect, and the connection from the E-PG neurons onto the P-EN neurons likely also recruits inhibition. Overall, we hypothesize that other neurons in the ellipsoid body and protocerebral bridge (Wolff et al., 2015) likely influence the exchange of information between these two neuron types.

Consistent with our conceptual model (Figure 1H), our electrophysiological results showed that individual P-EN neurons are tuned to heading and rotational velocity. Further, our functional connectivity results are supportive of a recurrent loop between the P-EN and E-PG populations, albeit perhaps with greater complexity than proposed by our simple conceptual model. This loop would, in the model, imply that the P-EN population inherits a bump of activity from the output of the E-PG population in the bridge, and that P-EN activity leads the E-PG bump in the ellipsoid body. To understand the spatiotemporal relationship between the two populations, we performed two-color calcium imaging (Dana et al., 2016) in flies walking in darkness. We expressed GCaMP6f in one population and jRGECO1a, a red indicator, in the other (Figure 8A). We tested for bleed-through and cross talk between the two channels (Sun et al., 2017), and verified clean color channel separation (see Figure 8—figure supplement 1 and Materials and methods for details; Sun et al., 2017). We also checked that our results were qualitatively consistent across the calcium indicator combinations we used and pooled the data for both color combinations in the results presented below, color-coding data according to the calcium indicator with which they were obtained in Figure 8.

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Imaging in the protocerebral bridge revealed two bumps of activity in the E-PG and P-EN neurons, one on each side of the bridge (Figure 8B), as predicted by our working model. To analyze the imaging stacks, we subdivided each half of the protocerebral bridge into nine regions of interest, one for each glomerulus (Figure 8C) and compared the activity in those regions over time to the rotational velocity for single trials (Figure 8D) and across trials (Figure 8E). For both neuron types, the bump intensities, but not widths, increased with increasing angular velocity across trials (Figure 8E,F). Although the P-EN bump intensity increased for both turn directions, which was unexpected, the increase was larger for ipsilateral turns than for contralateral turns (Figure 8—figure supplement 2A, top), a mirror-symmetry consistent with imaging results in the noduli (Figure 2) and with single cell electrophysiology (Figure 4B). The bump half-widths spanned ~2 glomeruli, which, if projected to the ellipsoid body, would lead to an activity width of 90 degrees. Consistent with our model, in which E-PG neurons carry the heading representation to the protocerebral bridge (Figure 1H), and in contrast with the activity of the P-EN subpopulations, there was no difference between E-PG activity for ipsilateral versus contralateral turns at any turn velocity (Figure 8—figure supplement 2A, bottom).

To determine the spatiotemporal relationship of the E-PG and P-EN bumps, we carefully examined their relative positions over time and across rotational velocities. To facilitate this comparison, we simultaneously registered both populations with respect to the E-PG bump in the right half of the bridge. We then binned these registered traces by rotational velocity. Across velocities, the registered E-PG and P-EN activity bumps overlapped, but with the P-EN bumps slightly lagging the E-PG bumps (Figure 8E,G, see Materials and methods for further details on the registration and binning procedures). The lag led to bump offsets of approximately one glomerulus (the equivalent of 45 degrees in the ellipsoid body) at 120–150°/s, the fastest rotational velocity bin considered (and the bin with the greatest offset), whether measured by the population vector average or by peak difference (Figure 8G). This offset was also consistent across different fly lines (Figure 8—figure supplement 2B). While the substantial overlap between the two bumps suggests that the heading representation in the two populations is, in fact, roughly coincident, the observation that P-EN activity follows E-PG activity by one glomerulus on both sides of the bridge was not predicted by our simple model. This lag may be due to the subtleties of connectivity between the E-PG neurons and P-EN neurons (as hinted at, for example, by E-PG presynaptic specializations in the ellipsoid body [Figure 7B] and by the complex responses seen in our functional connectivity experiments [Figure 7D, inset]) or, simply, due to delays caused by neural time constants (see model below).

Next, we sought to investigate the relationship of the P-EN and E-PG population activity in the ellipsoid body, where, the conceptual model would suggest (Figure 1H), P-EN activity should lead E-PG activity. To do so, we once again performed two-color imaging of the two populations (Figure 9A). As in the protocerebral bridge, we observed bumps of activity in both neural populations (Figure 9B). We then subdivided the activity into 16 regions of interest, corresponding to the number of distinct E-PG arborization sectors in the ellipsoid body (Figure 9B, bottom; [Wolff et al., 2015]) and compared the activity in those regions over time (Figure 9C). Bump amplitudes, but not half-widths, varied with angular velocity (Figure 9D,E), and both bumps were ~100° wide (full width at half maximum, Figure 9E).

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To compare E-PG and P-EN bump positions in the ellipsoid body across time, we performed a registration and binning procedure similar to the one used in the protocerebral bridge (see Materials and methods). Here, we used the E-PG bump in the ellipsoid body to align averaged calcium transients binned by the fly’s angular velocity (Figure 9F, standard deviations shown in Figure 9—figure supplement 1). As the fly turned, the relative position of the E-PG and P-EN bumps around the ellipsoid body changed. At angular velocities less than 30°/s, all flies showed bump offsets of less than 6° (2.5 ± 2.1°, N = 10 flies, Figure 9F,G). However, at rotational velocities above 30°/s, P-EN population activity separated from E-PG activity, with the P-EN bump leading the E-PG bump around the ellipse (Figure 9G, Video 2). When the red indicator was expressed in the E-PG neurons and the green indicator in the P-EN neurons, the bump offset exceeded 15 degrees of separation for turns faster than 90°/s regardless of turn direction and regardless of whether the fly was angularly accelerating or decelerating (20.7 ± 11.7° at 150–180°/s). When, instead, the red indicator was expressed in the P-ENs, the lead-lag behavior was qualitatively similar, though the shift only rose to 7.3 ± 7.2°/s at 150–180°/s (Figure 9—figure supplement 2). As jRGECO1a's kinetics are different from those of GCaMP6f, a difference in the observed shift across color combinations is to be expected (Dana et al., 2016). Thus, we concluded that the P-EN bump leads the E-PG bump in the ellipsoid body during turns, consistent with the conceptual model.

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Our conceptual model (Figure 1H) suggested that the anatomical offset between the E-PG and P-EN populations would move the activity bump in both populations during turns, thereby integrating rotational velocity to compute heading. However, it offered few predictions for how the feedback loops in the circuit might shape bump dynamics. In our two-color imaging experiments, we observed the hypothesized P-EN to E-PG activity offset, but the shift was small, between 10° and 30° at the highest angular velocities. Further, the E-PG activity in the bridge was consistently advanced from the P-EN activity by an offset that could exceed one glomerulus, which is half of the typical bump full width at half maximum in the protocerebral bridge. Finally, our functional connectivity experiments suggested that E-PG to P-EN connections may involve not just direct excitation, but the likely recruitment of inhibition as well. Thus, the simple conceptual model could not sufficiently explain the circuit dynamics we observed.

We therefore designed and simulated a firing rate model that built on the known facts about the circuit’s topology. We separated the P-EN neurons into two subpopulations of nine neurons each (one neuron per protocerebral bridge glomerulus, see Materials and methods), one population for neurons with dendritic projections on the right side of the protocerebral bridge, and another for neurons with dendritic projections on the left side. 54 E-PG neurons (three neurons per protocerebral bridge glomerulus) locally excite P-EN neurons, with each P-EN neuron innervating one glomerulus of the protocerebral bridge. In return, P-EN neurons excite E-PG neurons in the ellipsoid body, but with a shift in either the clockwise or counterclockwise direction, depending on whether they belong to the left or the right subpopulation. Further, we stipulated that the E-PG neurons uniformly and strongly inhibit P-EN neurons (all connections shown in Figure 10A; see Figure 10—figure supplement 1A for the connectivity matrix; see Discussion for possible inhibitory pathways). Such inhibition was a pre-requisite to obtain a stationary bump of activity and a near-linear integration of velocity inputs (Figure 10—figure supplement 1B; see below). Finally, turns were initiated by an external input to the model that uniformly excites either the right or left P-EN subpopulations (Figure 10A, bottom).

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This rate model, which is reminiscent of past models of mammalian head direction cells (Skaggs et al., 1995; Xie et al., 2002; Zhang, 1996), albeit with key differences in connectivity, captured the essence of much of what we observed in our experiments. Simulated firing rate curves of P-ENs in response to rotational velocity input were sigmoidal with an inflexion point at 0°/s, matching our experimental observations (compare Figure 10—figure supplement 1C and Figure 4C). Further, the simulations indicated that the circuit functions as a ring attractor, enforcing a unique bump across the E-PG population, consistent with previous experimental results (Seelig and Jayaraman, 2015, Kim et al., 2017) (Figure 10B,C). Our model does not feature an attractor network within the E-PG population. Rather, E-PG activity is driven entirely by P-EN neurons and the persistent bump of activity arises from a feedback loop between these two neural populations. As a result, the activity and bump amplitudes of both populations are sensitive to velocity input, as observed experimentally (Figure 10—figure supplement 1C,D).

The firing rate model also produced offsets in the activity of the E-PG and P-EN neurons in the ellipsoid body and in the protocerebral bridge, qualitatively matching our experimental observations. In the simulations, the offsets are due to the neural time constants (see Materials and methods); the bump in the P-EN population is not updated immediately after the E-PG bump shifts. We thus observe a phase difference in the bridge, with the P-EN bumps lagging the E-PG bumps, as observed experimentally (Figure 8G). This lead-lag relationship was reversed in the ellipsoid body, where the P-EN population drives the E-PG population. The neural constants are expected to be largely independent of the bump’s velocity. Consequently, when the E-PG bump in the protocerebral bridge, for instance, traveled further in a given time window (as during a fast turn), the P-EN bump lag and phase difference increased, consistent with experimental observations (Figure 9F, Figure 10D).

For an animal to reliably track its heading over long periods of time, it must accurately integrate its rotational velocity. Thus, in a robust system, the heading representation should update with an internal rotational velocity that matches the animal’s external velocity. Indeed, the architecture of our model ensures a quasi-linear relationship between bump rotation and steady velocity input (Figure 10E) up to an inherent saturating angular velocity. This linearity mainly depends on an appropriate balance of local E-PG to P-EN excitation, and inhibition onto the P-EN neurons (Figure 10—figure supplement 1B). When we drove the P-ENs with a simulated but realistic time varying velocity input (Figure 10—figure supplement 2), we saw that the bump in the model closely followed the integrated external velocity, reliably tracking the animal’s heading (see Figure 10F, Figure 10—figure supplement 1E,F). The errors that did arise were due to inaccurate integration of small input velocities. The limited number of P-EN neurons in the model (nine per subpopulation) caused the bump to ‘stick’ at individual P-EN neurons until the input velocity provides enough drive to free it, at values greater than 15°/s. The integration error, the difference between the bump position and the integrated velocity input, followed a distribution centered on zero, with a variance that increased over time (see Materials and methods). For short times (on the order of 10 s or less), the variance of the distribution was small, that is, the error between the animal’s heading and the animal’s representation of its heading was likely to be small (Figure 10—figure supplement 1E). This error was influenced by the neural time delays of the angular integration circuit (Figure 10—figure supplement 1F). For longer time scales, the error distribution broadened (Figure 10G) so that the variance increased linearly, a characteristic feature of a diffusion process (that is, the heading representation diffused away from the true heading over time). Overall, the linearity of the heading response to input and the small value of the diffusion coefficient suggest that this simple model of only three neural populations and E-PG to P-EN inhibition, which may be mediated by an additional and as-yet-uncharacterized neural population, can not only maintain a bump of activity, but can accurately update that bump position to reflect the animal’s heading.

Both the conceptual model (Figure 1H) and the quantitative model (Figure 10) rely on P-EN input to the E-PG neurons to maintain and move the bump. To assess the impact of the loss of P-EN input on E-PG bump strength and movement, we imaged E-PG activity while impairing P-EN synaptic transmission. To that end, we used shi^TS, a temperature-sensitive mutation of the Drosophila gene encoding a dynamin orthologue, which blocks vesicle endocytosis at elevated temperatures (Kitamoto, 2001). We compared E-PG activity between control flies expressing shi^TS in an empty promoter Gal4 and flies expressing shi^TS in two different P-EN lines, R37F06 and VT008135. For each line, we compared the activity at room temperature (permitting synaptic transmission) to the activity at the restrictive temperature of 31°C, where vesicle reuptake and, thus, synaptic transmission should be blocked (Figure 11).

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We first checked that the flies’ behavior was consistent across genotypes. Indeed, the walking statistics were similar at a given temperature across GAL4 lines (see, for example, the distributions for two flies at 31°C in Figure 11Ai), though we consistently observed a strong increase in forward walking at the higher temperature (Figure 11Aii). In experiments with control flies, we found that this increased forward velocity reliably led to a high-amplitude E-PG activity bump at the higher temperature (Figure 11B, top). As expected, the bump position slowly drifted away from the fly’s heading in darkness, but otherwise tracked the fly’s rotations (Figure 11B, bottom). In contrast, in flies that expressed shi^TS in P-EN neurons, the bump was significantly weaker and more variable (Figure 11C), although these flies walked just as actively at the higher temperature as control flies.

We next quantified the effects of blocking P-EN neurons on E-PG bump amplitude. At room temperature, the distribution of E-PG bump amplitude across trials was similar for the control flies and for flies that expressed shi^TS in the P-EN neurons (Figure 11D). At 31°C, however, when the flies became more active and increased their forward velocity, the amplitude distribution of the control flies shifted to higher values. In contrast, in the shi^TS P-EN flies, where this activity increase was accompanied by a synaptic block in P-EN neurons, the bump amplitude distribution remained relatively unchanged, or even decreased (Figure 11D).

We looked to see if this reduced amplitude could be explained by our firing rate model. The model demonstrated that two recurrent E-PG/P-EN loops are sufficient to maintain and shift a bump of activity (Figure 10). If connections between the model’s loops are broken, the bump would be expected to disappear. To explore the consequences of weakening the P-EN to E-PG connections instead of breaking them entirely, we continuously tuned the weights down to 0 from their initial value (thereby modifying the P-EN to E-PG synaptic efficacy) and observed a range of simulated activity profiles in the ellipsoid body (Figure 11E, Figure 11—figure supplement 1C). When the synaptic efficacy was close to 0, activity in the network was relatively uniform and did not localize into a bump. At higher P-EN to E-PG synaptic efficacies, network activity localized into a bump, and its amplitude increased as the efficacy of the synaptic connection between P-EN and E-PG neurons increased. This critical role for the P-EN-to-E-PG connection in maintaining bump strength was consistent with the reduction in bump amplitude that we observed experimentally across flies and genotypes when P-EN output was impaired (Figure 11D).

Finally, we examined the reliability of the E-PG bump in tracking turns across flies and across conditions. Individual turns were defined as continuous periods where the rotational speed exceeded 15°/s. To guarantee that we could reliably track the bump across these turns, all turns with a PVA amplitude below a set signal-to-noise threshold were excluded from this analysis (Figure 11—figure supplement 1A, see Materials and methods). We then compared the change in PVA position to the change in the fly’s heading over the course of these turns (Figure 11—figure supplement 1B). We fit the data with a simple linear model and extracted R² values to gauge how reliably the PVA tracked turns (Figure 11—figure supplement 1D,E). These R² values were, on average, lower for the P-EN lines than for the control line, suggesting that disrupting the P-EN synaptic output affects the reliability of the E-PG bump to track turns. However, while our conceptual model and our simulation suggest that the E-PG bump should move less when P-EN output is reduced (Figure 11—figure supplement 1C), we instead observed that the E-PG bump often drifted at the restrictive temperature in the shi^TS P-EN flies (see Discussion), as can be seen in part from the wide range of PVA changes seen during turns (Figure 11—figure supplement 1B).

Overall, although our shi^TS results could not clarify how exclusive a role the P-EN neurons play in moving the E-PG bump in darkness, they revealed the importance of P-EN activity for maintaining the E-PG bump’s strength and stability, consistent with the assumptions of our model.

For a circuit mechanism in which phase relationships and conjunctive coding are important, calcium imaging may seem an unreliable arbiter of truth. Somatic single cell recordings, on the other hand, can be hard to interpret given the intricate projection patterns of fly neurons and the compartmentalization of information processing that this can produce (Yang et al., 2016). However, we found the results from our calcium imaging and electrophysiology experiments in P-EN neurons to be in broad agreement. The electrical signature of P-EN responses to angular and forward velocity mirrored what we saw with calcium imaging in the noduli. The measured width of a single P-EN neuron’s receptive field (~60°) was lower than that observed with calcium imaging (~110°, Figure 9 and Figure 9—figure supplement 2), but this may arise from the slow decay kinetics of calcium indicators. One inconsistency between results, however, related to the imaging of neural activity in the protocerebral bridge. Based on imaging in the noduli (Figure 2) and electrophysiology (Figure 4 and Figure 5), we expected that turns in one direction would evoke a steady decrease in activity on the other (contralateral) side of the bridge with increasing rotational velocity. Instead, imaging in the bridge showed a mild increase in activity at higher velocities (Figure 8), albeit while preserving the expected asymmetry between the ipsi- and contralateral side (Figure 8—figure supplement 2). We hypothesize that this calcium signal might represent synaptic inputs to the P-ENs more than their spiking activity.

Blocking P-EN output using shi^TS had two effects on the E-PG bump: Its amplitude was reduced and its position sometimes changed dramatically during small turns (visible as an increase in variability and low R² for the correlation of changes in heading versus PVA in Figure 11—figure supplement 1B,D). The bump amplitude decrease in shi^TS flies at high temperature can be readily explained by the reduction in synaptic input to the E-PGs — indeed, in our firing rate model (Figure 10) P-EN input is essential to the maintenance of the E-PG bump. Several factors may explain why the E-PG bump did not completely disappear during this manipulation. First, cell-intrinsic properties of the E-PG neurons may contribute to the persistence of activity in those neurons even in the absence of external input. Second, the shi^TS block may have been incomplete (Thum et al., 2006), meaning that there was sufficient P-EN drive even at high temperatures to keep the E-PG bump alive. Third, we cannot rule out the possibility of gap junctions between P-EN and E-PG neurons, which our experiments would not block. Finally, as mentioned above, other neuron types, such as the PB_G1–8.s-EBt.b-D/Vgall.b (Wolff et al., 2015), may also provide synaptic input to the E-PG population in the ellipsoid body. Some of these possibilities have been suggested in a recent study that used the anatomy of protocerebral bridge neurons to create a spiking model that generates ring attractor dynamics (Kakaria and de Bivort, 2017).

Further, our conceptual and firing rate models would imply that if the P-EN to E-PG connections were entirely removed, E-PG activity would be unable to follow the fly’s turns. However, the E-PG activity does still track the fly’s turns at high temperature, when the P-EN synaptic output should be blocked. This may, once again, be the result of an incomplete block. We speculate that one reason that the E-PG bump makes large movements across the ellipsoid body even during small turns is that a reduction in bump amplitude destabilizes the compass representation. Thus, fluctuations in the activity of the E-PGs elsewhere in the ellipsoid body may exert a greater influence on the movements of the bump than under normal conditions, when activity in distant E-PGs is likely to be suppressed (Kim et al., 2017). Yet another possibility that could explain the bump’s movements is raised by a parallel study, which provides further evidence for P-ENs serving a role in angular integration and describes a second subtype of P-EN neurons that likely also influences the position of the E-PG bump (Green et al., 2017).

The coordinated activity of the E-PG population and its control by the P-EN population when the fly turns are strongly evocative of a compass. The animal could, in principle, use such a neural compass to tether its actions to local landmarks or other sensory cues during navigation, and maintain its bearings in the temporary absence of such cues (Neuser et al., 2008). Consistent with this idea, the PVA computed with E-PG population activity tracks the fly’s heading quite accurately even in darkness. However, we do not yet know how downstream circuits read out E-PG population activity. Thus, although the PVA metric is a useful representation of E-PG compass-like activity, whether downstream circuits perform similar computations to extract the fly’s heading is unclear. Further, we derive the PVA by combining the strength and angular position of activity in the E-PG population. Although both these features of E-PG activity likely influence downstream neurons, their specific influence on such neurons will depend on the precise connectivity of the circuit, something that a combination of functional connectivity studies and electron microscopy may reveal in time. Although there is considerable evidence across insects suggesting that CX neurons influence action initiation and turning movements (Bender et al., 2010; Martin et al., 2015), the connection of E-PG and P-EN neurons to the largely unidentified class of CX neurons that drive behavioral decisions is as yet unclear.

We have focused here on the effects of self-motion cues on bump movement, to which end most of our experiments were conducted with flies walking in the dark. However, E-PG activity is strongly influenced by visual cues, as evidenced by the fact that cue jumps can reset the bump position (Seelig and Jayaraman, 2015; Kim et al., 2017). The angular velocity representation of P-EN neurons, by contrast, seemed unaffected by the presence of closed loop visual feedback (Figure 4—figure supplement 1). Thus, while we have suggested a circuit mechanism for updating heading representation in the dark using self-motion signals, we anticipate that strong sensory inputs, including those from visual cues, control updating in other circumstances. For example, we have previously observed that the ring neurons retinotopically respond to visual cues (Seelig and Jayaraman, 2013). As the putative ring neuron axons arborize in the ellipsoid body along with the E-PG dendrites, it may be possible for them to convey visual information to the E-PG neurons, influencing the movement of the bump of activity. Further, we suggested above that the E-PG to P-EN connection in the protocerebral bridge may be indirect and recruit sources of inhibition. There exist a few classes of bridge interneurons, the so-called PB_G6-8.s_G9.b, PB18.s-GxΔ7Gy.b and PB18.s-9i1i8c.b neurons (Wolff et al., 2015), which may serve as intermediaries in E-PG to P-EN connections (Kakaria and de Bivort, 2017). Future studies should help clarify their role in the compass network.

Fly E-PG neurons share several characteristics with mammalian head direction cells. Both head direction cells and E-PG neurons maintain one stable bump of activity and both track the animal’s heading in darkness, a feature that is well described by appropriately wired ring attractor models. Rodents that are deprived of proprioceptive and motor efference signals, as in passive transport experiments (Stackman et al., 2003), show impaired heading representation. To update their heading in darkness, head direction cells in rodent thalamic nuclei and post-subiculum are thought to depend on angular velocity input from the vestibular system, mediated by the dorsal tegmental nucleus (Bassett and Taube, 2001). Although 75% of neurons in this region were found to encode angular head velocity, only about a third of those did so in the mirror-symmetric, turn-direction-selective fashion of Drosophila P-EN neurons that we describe here.

Individual P-EN neurons were deterministic in their left-right mirror-symmetric rotation tuning, but diverse in the range of rotational velocities that their tuning curves spanned. Indeed, the measured bandwidth of individual P-ENs ranged anywhere between 30 and 270°/s. This diversity may reflect the diversity of tuning of the three to four P-EN neurons that innervate each protocerebral bridge glomerulus (estimated from cell body counts [Wolff et al., 2015]). Such a range of sensitivities and bandwidths would permit a more precise tracking of the flies’ turns across a wide range of rotational velocities.

The origins of angular velocity responses in P-ENs are as yet unclear, but these responses show a latency relative to the fly's turning movements that we estimated to be ~150 ms, suggesting that they arise from proprioception rather than motor efference. Anatomically, both the two halves of the protocerebral bridge as well as the two noduli are mirror-symmetric structures innervated by a number of neuron types in a lateralized manner (for example, PB_G2-9.b-IB.s.SPS.s [Wolff et al., 2015]), making them likely candidates for receiving such rotation-tuned input. In the cockroach, neurons encoding angular as well as forward velocity have been recorded in the fan-shaped body (Guo and Ritzmann, 2013; Martin et al., 2015), a substructure of the central complex that is evolutionarily conserved in flies. Of note, only one of the forty turn responsive neurons in the latter study showed bidirectional modulation, with excitation for turns in the preferred direction and inhibition for turns the other way, a hallmark of the P-EN neurons. These studies, which relied on extracellular recordings and did not identify cell types, found that changes in spike rate regularly preceded locomotor changes instead of tracking them as we found for the fly P-ENs. If we assume that neurons of the type recorded in the cockroach also exist in the fly, it is not yet clear whether the P-EN/E-PG compass network that we describe here exploit advance information about expected changes in angular velocity.

A striking aspect of the fly compass system is its structural symmetry. Mirror symmetry is a prominent feature of the anatomical layout of the protocerebral bridge. The developmental origins of the anatomical positions of central complex neurons have been the focus of numerous studies (recently, for example, Boyan and Liu, 2016). However, although the two sides of the protocerebral bridge and the noduli are tuned to rotations in opposite directions, maintaining symmetry at the large scale, the activity of the E-PGs and P-ENs at the scale of bridge glomeruli breaks this symmetry. During a turn, bumps of activity propagate through the left and right sides of the bridge in parallel, in a manner reminiscent of windshield wipers, rather than obeying mirror symmetry. This pattern of activity, together with the connectivity of protocerebral bridge glomeruli and ellipsoid body sectors (see schematic in Figure 1G) ensures that the E-PG bump moves smoothly around the ellipsoid body when the fly turns.

More broadly, topographical organization is a striking feature of many sensory circuits, but structure often follows computational function in neural circuits in the central brain as well. The feedforward pathways to and from the Mauthner cell make clear these neurons role in rapid escape behavior (Hale et al., 2016), and the parallel delay loops of the barn owl auditory system and the electric fish point to their comparative roles in localizing prey (Konishi, 2006). The anatomical shift of the P-EN neurons with respect to the E-PG neurons (Wolff et al., 2015) provided an immediate clue to a potential structure/function relationship, that of a mechanism for shifting the bump of E-PG activity to update their internal representation of heading. The fact that topography often matches topology in the small fly brain makes the system ideal for the identification of circuit mechanisms underlying complex computations. Only time —and perhaps large scale circuit reconstruction efforts (Denk et al., 2012)— will tell whether such network motifs are also present, but perhaps better hidden, in the more distributed circuits of much larger brains.

Imaging stacks for R60D05 and R37F06 were obtained from the FlyLight imaging database (Jenett et al., 2012). As the protocerebral bridge expression was significantly dimmer than the ellipsoid body expression, the volume was divided in two, with the ellipsoid body in the front half and the bridge in the back. The two half volumes were then collapsed into maximum intensity projections, and the contrast and brightness independently varied to best show the individual structures. The two images were then combined and used to generate a third maximum intensity projection, which is shown in Figure 1.

Flies were prepared for imaging as described in (Seelig and Jayaraman, 2015). Briefly, flies were affixed to a metal shim that held their head and thorax in place while allowing their legs and abdomen to move freely. The fly’s head was immersed in saline of the following composition (in mM): NaCl (103), KCl (3), TES (5), trehalose 2 H₂O (8), glucose (10), NaHCO₃ (26), NaH₂PO₄ (1), CaCl₂ 2 H₂O (2.5), MgCl₂ × 6 H₂O (4). The head capsule was opened and soft tissue removed, exposing the brain. For imaging the noduli or ellipsoid body, the fly’s head was positioned so that the top of the head was level. For imaging the protocerebral bridge, the head was pushed down so that the back of the head was at a ~30° angle. The flies were then suspended over a free-floating foam ball and allowed to walk freely while an immersion objective was lowered into the saline. An 8 mm diameter, 92 mg ball was used for the fly treadmill.

Red and green indicator calcium imaging was performed on a custom two-photon microscope running ScanImage 2015 and similar to the one described in (Seelig and Jayaraman, 2015). An 875 nm longpass filter was placed in the incident light path to cut out shorter wavelengths from the laser that otherwise contaminated the red channel. A primary dichroic at 705 nm was used to direct emitted light to the red and green PMTs, and a secondary dichroic at 594 nm split the light to the red and green channels. 50 nm bandpass filters centered around 525 and 542 nm were placed in front of the red channel and a 90 nm wide filter centered at 625 nm was set in front of the green channel. For five of the flies used for ellipsoid body imaging, a 660 nm primary dichroic was used instead of the 705 nm filter.

All imaging was performed using a 1020 nm, femtosecond pulsed laser at ~20 mW power. For the ellipsoid body and noduli imaging, 256 × 256 pixel volumes were obtained. The volumes were ~50 μm on a side and consisted of 6 Z planes, each spaced 8 μm apart and chosen so as to tile the ellipsoid body and noduli. Volumes were obtained at a rate of 11.4 Hz. For protocerebral bridge imaging, 256 × 256 volumes were again obtained, but they were now ~200 μm on a side and consisted of 6 or 12 Z planes imaged at 11.4 Hz and 6.2 Hz, respectively. An 8 μm spacing was used for the six plane volumes, and a 3 or 4 μm spacing was used for the 12 plane volumes. The ball position was tracked at 200 Hz and synchronized to the calcium imaging using TTL pulses triggered after each frame grab.

Recordings began as soon as the fly was placed on the ball and the software was initialized, usually within five minutes, and never longer than 15 min. If the brain was visibly moving more than ~5 µm or if the fly ceased to move within the first four trials, the experiment was discarded. Otherwise, all trials were analyzed.

The timestamps for each imaging frame were found from the TTL signal, and the behavioral data at the point that corresponded to the end of each volume acquisition was tabulated to create downsampled behavioral data to match the imaging data. Rotational, translational, and side slip velocities were then calculated by taking the difference of the fly’s heading, total translation, or side translation across each time point and dividing them by the time between points. Velocities were also Savitzky-Golay filtered over 11 frames with a third order polynomial to smooth out artifacts due to downsampling.

We defined ellipsoidal regions of interest over each nodulus, calculated ΔF/Fs, and then computed the difference in activity between the two sides. Rotational velocities were convolved with the function:

using time constants for GCaMP6f obtained in response to 10 action potentials in dissociated mammalian neurons: τ_on = 0.13 s and τ_off = 0.63 s (Chen et al., 2013). The convolved velocities were then correlated with the net activity. Correlations were not significantly different if other time constants from the same study were used.

For linear regression analysis, rotational velocities were separated into right and left rotations by taking the absolute values of all negative or, respectively, the positive rotational velocities. The rotational velocities and forward velocity were fit to determine regression coefficients for Z-scored locomotor parameters (predictors: v_{Rot right}, v_{Rot left}, v_For), using the ΔF/F signal of a given ROI (left or right nodulus) as observation. Bin size for all parameters was 25 ms. Time shifts (−375 to 750 ms in 25 ms steps) were introduced between observation and predictor variables to get a sense of neural responses over time.

We should note that the GAL4 driver line that we used for these experiments, R37F06, also contains PB_G2-9.s-FBl3.b-NO₂V.b (PFN_V) neurons (Wolff et al., 2015). To avoid potential contamination from this population, we only imaged the top plane of the noduli that contain the P-EN arbors in the NO₁ subdivision. NO₂, where the PFN_Vs arborize, is distinct from and more ventral to NO₁. The expression of the PFN_V neurons was often too weak to see, but, when visible, NO₁ and NO₂ could be clearly distinguished.

The fly was anaesthetized on ice and transferred to a cold plate at 4°C. The fly’s proboscis was pressed onto its head and immobilized with wax. The fly’s head and thorax were UV-glued to a plastic shim, the chamber was sealed with grease, the head covered with saline, and the head capsule opened. The saline used for electrophysiology was the same as used for imaging, except that the concentration of CaCl₂ was 1.5 mM instead of 2.5 mM. The fly was positioned on an air-supported ball and the walking velocity of the fly was monitored using a camera system (Seelig and Jayaraman, 2015). For all electrophysiology experiments, we used an 8 mm diameter, 47 mg polyurethane foam ball. Ball movement was recorded at a sampling rate of 4 kHz. Flies were either recorded in darkness, or given closed loop control over a 15° wide blue vertical stripe, presented on a cylindrical LED display spanning 240° in azimuth and 120° in elevation (Reiser and Dickinson, 2008). For all experiments presented in this paper, the gain of the closed loop was set to a natural gain of 1, meaning that a 90° rotation of the ball was mirrored as a 90° counter rotation of the stripe in the visual arena. The gap of the arena (in the back of the fly) was ignored, i.e. the stripe ran smoothly from one side of the gap to the next as if the space in between did not exist. For analysis, stripe positions on the 240° arena were mapped linearly to an arena spanning 360°.

For electrophysiology experiments, GFP was expressed in P-EN neurons and visually guided single cell recordings were performed under epifluorescence and IR illumination. Whole-cell and loose patch recordings from P-EN neurons were performed with a recording solution of the following composition (in mM): K-aspartate (140), HEPES (10), KCl (2), EGTA (1.1), CaCl2 (0.1), MgATP (4), Na3GTP 0.5), phosphocreatine (5), and biocytin (0 to 6). The brain was perfused with oxygenated saline containing 1.5 mM CaCl₂. The faithfulness of spike rates reported with whole-cell patch-clamp recordings was verified by extracellular loose patch recordings (Supplementary file 1). Pipette resistance and capacitance was compensated. For whole-cell recordings, recording quality was monitored throughout the experiment through positive and negative current injections. Access resistance and liquid junction potential was not compensated for. Data was acquired at 20 kHz and low-pass filtered at 4 kHz. Cells were filled with Alexa dyes and neuron identity and soma location was confirmed by epifluorescence imaging and/or post-hoc confocal imaging of PFA-fixated brains.

The input resistance R_in was estimated from fitting an exponential decay to the membrane potential after the offset of current injections. Since the offset of a hyperpolarizing current pulse often resulted in rebound spiking and a concomitant overestimation of R_in we fitted the offset of depolarizing current injections to derive the passive electrical properties. P-ENs are unipolar neurons with a small soma (diameter <5 μm), a thin primary neurite, and a high input resistance (1.9 ± 0.8 GΩ) close to typical patch clamp seal resistances. Since in that scenario the two resistances function as a voltage divider, the measured membrane potential might be erroneous. We thus performed loose patch recordings from P-ENs to determine their firing rates in an unperturbed recording configuration (3.9 ± 2.6 Hz during standing epochs) and aimed at adjusting the resting membrane potential in whole-cell recordings to a value that would roughly yield similar spike rates. The bias current injected to that end was on average −5.8 ± 3.6 pA. See Supplementary file 1 for quantification of electrophysiological properties. Patch-clamp recordings of cells with lower input resistance, higher membrane potential, and/or higher bias currents than those reported in Supplementary file 1 were excluded from analysis, as were cells in which the access resistance was too high to allow the unambiguous detection of action potentials. Both whole-cell and loose patch recordings were terminated (and/or late trials excluded post-hoc) if the spike frequency increased (or decreased) notably, whether it was because of unfavorable changes in signal-to-noise ratio, sudden depolarizations in the whole-cell configuration, or partial break-ins in loose patch recordings. All reported values for the cellular membrane potential are not corrected for liquid junction potential.

After the end of a patch-clamp experiment flies were carefully detached from the holder and their brains dissected out. After 10 min fixation in 4% PFA in 0.1M phosphate buffer at room temperature, brains were washed three times for five minutes each in phosphate buffer and mounted in Vectashield on a microscopy slide. Samples were imaged on a Zeiss 710 confocal microscope with a 63x objective. Since P-EN arborizations span the entire dorso-ventral axis of the fly brain, a given set of frames encompassing an individual neuropile was merged in a maximum z-projection and adjusted for brightness and contrast. These individual neuropile projections were then max-projected to yield the overview image shown in Figure 3A.

For both loose patch as well as whole-cell recordings, the recorded voltage was bandpass filtered (50–1000 Hz) and a threshold was set for automatic spike detection. The threshold was set individually for each recording and, if necessary, manually adjusted for individual sweeps (10–300 s). Only recordings with a reasonable signal to noise ratio and sufficient robustness to the setting of the spike threshold were included in the final dataset (see also Figure 4—figure supplement 2).

The fly’s velocities were calculated from the displacement of the ball as reported by the treadmill (Seelig et al., 2010).

To quantify the neural response to rotational velocity, we fit sigmoidal tuning curves to membrane potential and spike rates as a function of rotational velocity. All parameters were binned into 50 ms windows. The membrane potential was 50 Hz lowpass filtered and spike rates were smoothed with a 1 s Gaussian window with a half-width of 0.35 s. After shifting the neural responses by the cell’s respective response lag (see below) to align them with the rotational velocities they correlated most strongly with, we excluded all periods in which the fly was not walking. Subsequently, mean values were calculated for 12°/s wide rotational velocity bins and fit with a sigmoid of the form

with r_max being the neurons maximum response, s the slope of the tuning curve, v_rot the rotational velocity, v₀ the lateral displacement of the curve on the x-axis and r_min the minimum response of the neuron. The number of samples that contributed to each mean were used as weights.

Heat maps for spike rates and membrane potential as a function of rotational and forward velocity were constructed from non-overlapping 20 ms bins of data, similar to (Guo and Ritzmann, 2013) except that velocities were not smoothed. Spike rates were smoothed with a 1 s Gaussian window with a half-width of 0.3 s and the membrane potential was 50 Hz low-pass filtered. For each time bin, values were assigned to a field in a two-dimensional array depending on the fly’s rotational velocity (in a 12°/s grid) and forward velocity (in a 1 mm/s grid). The heat map represents the mean value of each of those fields containing at least 50 time bins (1 s of data).

Since P-EN neurons tend to spike at rest, spike-triggered averages (STAs) of rotational velocities were constructed from only those spikes that occurred during 2 s epochs with rotational velocities exceeding 40°/s at any point in time.

For linear regression analyses, the membrane potential was 50 Hz low-pass filtered. Rotational velocities were separated into right and left rotations by taking the absolute values of all negative or, respectively, positive rotational velocities. The rotational velocities, membrane voltage and spike counts were subsequently binned in non-overlapping 20 ms windows. Generalized linear model regressions were fit to determine regression coefficients for Z-scored locomotor parameters (predictors: v_{rot right}, v_{rot left}, v_{rot right}³, v_{rot left}³), using either membrane potential or spike count as observations. We assumed a normal distribution for the membrane potential (making the procedure an ordinary linear regression) and a Poisson distribution for the spike count (modeling the logarithm of the spike count as a linear combination of the predictors). Time shifts (−2 to 2s in 20 ms steps) were introduced between observation and predictor variables to reveal possible delays between the neural response and the fly’s behavior (or vice versa). To calculate the autocorrelation of the rotational velocity, the same method was applied, using v_rot as predictor and observation both. In all cells, the regression coefficients for rotational velocity with neural responses followed a stereotypical time course. The neural response lag was defined as the time point of the maximum absolute difference of regression coefficients for ipsi- and contralateral rotations. Inclusion of forward velocity in the generalized linear regression did not yield consistent results across P-ENs. On average, coefficients at zero temporal lag were not different from zero (0.00 ± 0.08, one-sample t-test: p=0.99). Thus, we did not include forward velocity as a parameter for determining temporal properties of P-EN firing.

Heat maps for spike rates and membrane potential as a function of rotational velocity and the fly’s heading were constructed from non-overlapping 25 ms bins of data. In visual closed loop experiments, the heading angle was approximated as 2π minus the azimuthal position of the stripe. In experiments where the fly walked in darkness, the heading angle was calculated as the accumulated rotation of the fly. Spike rates were smoothed with a 1 s Gaussian window with a half-width of 0.3 s and the membrane potential was 50 Hz low-pass filtered. For each time bin, values were assigned to a field in a two-dimensional array depending on the fly’s rotational velocity (in a 12°/s grid) and the fly’s heading (in a π/4 grid). The heat map represents the mean value of each of those fields containing at least eight time bins (0.2 s of data).

For fitting tuning curves to the fly’s rotations and virtual heading, we restricted the analysis – for each cell individually – to the range of rotational velocities that was well sampled across all virtual heading angles to avoid strong biases in the sampling of particular heading angles at a given rotational velocity. To that end, we visually inspected grayscale heat maps of a sample size matrix analogously generated to the neural response matrix (see Figure 5—figure supplement 1B) and excluded all columns outside a well-sampled rectangle. Rotational velocity tuning curves were fit with a sigmoidal function as outlined above, except that rotational velocity bins were 12.5°/s wide (to fit with the margins of the heading ∪ rotational velocity array created for the heat map on the basis of which the velocities to be included were determined). Heading angles were binned into 32 bins of 11.25° width. Tuning to the fly’s heading angle θ was fit with a compound von Mises function of the form

with r_min being the minimum response of the neuron, g the rate modulation of the neuron, θ₀ the preferred heading angle, κ the concentration of the modulation around θ₀, and I₀(κ) the modified Bessel function of order 0. P-EN heading tuning often showed a peak and a trough (see Figure 5C), a phenomenon that was more pronounced on the level of the membrane potential, and was not well described by a unimodal von Mises function. Initial values for the parameters θ₀₁ and θ₀₂ were chosen to be angles of the peak and trough of the smoothed mean spike rates curves.

A rotation tuning and a heading tuning index was calculated for each cell. The rotation tuning index was defined as the R² value of the sample-size weighted spike rate fit to a sigmoidal function (see above).

The heading tuning index H was defined as the mean vector length of spike rates over all virtual heading angles and calculated according to the following formula:

with N being the number of heading bins (N = 32), θ_n the bin center, and r_n the mean spike rate in a given bin. The preferred heading angle was defined as the argument of H, i.e. the phase of the mean vector.

Chance-level statistics for rotation and head direction tuning indices of experiment averages were computed for each cell individually through a shuffling procedure (performed for 5/6 visual closed loop cells, since the 6^th fly did not sufficiently sample all heading quadrants for a wide enough range of rotational velocities). For each of the 1000 shuffling repetitions, a cell’s spike train was chopped into 1s fragments that were randomly re-assembled. The shuffled instance of the two tuning indices was calculated using the scrambled spike train. The significance level was set at the 95^th percentile of the shuffled rotation and heading indices.

Experimental epochs for analysis of heading tuning stability and extraction of conditional tuning curves were identified as follows: All recordings were partitioned in five minute trials for which two-dimensional sample size matrices were constructed as described above (see Figure 5—figure supplement 1B) and visually inspected for non-zero bin counts within a rectangular portion of this matrix that spanned all heading angles and at least 250°/s width of rotational velocities. Practically, we thus selected for epochs in which the flies rotated vigorously on the ball, visiting all possible heading quadrants by balanced turning about the right and left body axis. In one case, two such five minute trials were concatenated to obtain sufficient sampling. The same shuffling procedure as described above was performed for the experimental epochs, except that trials were now chopped in 0.5 s fragments. 6/7 epochs passed the significance threshold (p-value of the failed epoch: p=0.193).

To generate conditional tuning curves for a neuron’s tuning to the fly’s heading dependent on rotational velocity, separate tuning curves were fit to spike rate and membrane potential as a function of the fly’s heading for all epochs of positive versus negative rotational velocities, respectively. To generate conditional tuning curves for P-EN tuning to rotations, separate tuning curves were fit to the fly’s spike rate and membrane potential as a function of the rotational velocity for epochs when the fly’s virtual heading was aligned with the neuron’s preferred versus non-preferred heading quadrant. The preferred heading quadrant was determined from the unconditional tuning curve containing all data points (see Figure 5C for an example) and defined as all bins where the firing rate exceeded the half-maximal positive modulation, that is, the baseline (defined as the median firing rate across bins) plus half the difference between baseline and peak firing rate. The non-preferred quadrant was defined as all heading bins where the firing rate was below the median (see Figure 5—figure supplement 1D for visualization). Otherwise, the fitting procedure was the same as outlined above: Data was binned into non-overlapping 25 ms bins of data, spike rates were smoothed with a 1 s Gaussian window with a half-width of 0.3 s, membrane potential was 50 Hz low-pass filtered, rotational velocities were sorted in 12.5°/s bins, heading angles in 11.25° bins.

The calculation of conditional tuning indices was performed in an analogous fashion: The conditional rotation tuning index was defined as the R² value of the conditional tuning curve described above. The conditional heading tuning index was defined as the mean vector length of spike rates over all virtual heading angles, computed separately for rotations to the P-EN’s preferred versus non-preferred turn direction.

For quantifications presented in Figure 6E, rotational tuning was defined as the difference of the fitted neural response at −150 and 150°/s. Heading tuning was defined as the difference of the conditional tuning curves at the positions corresponding to the maximum and minimum of the unconditional tuning curve that was fitted to all data points irrespective of turn direction. To avoid blurring the boundaries of preferred and non-preferred heading quadrants over time, the analysis of conditional tuning curves presented in Figure 6 was performed on the 5–10 min experimental epochs presented in Figure 5—figure supplement 1C. In one case where two separate epochs had been identified for the same cell, only the first one was included in the analysis. For the analysis of the relative differences in membrane potential versus spike rate modulation presented in Figure 6—figure supplement 1, we used these same epochs from all whole cell recordings (at most one per cell) plus the experiment averages of the two neurons that showed significant heading tuning over the course of the experiment, but no sufficient sampling during short epochs.

Immunohistochemistry was performed largely as described in (Aso et al., 2014), but with several exceptions. Standard dissection, fixation and rinse conditions were used, as outlined previously. The concentrations of the two primary antibodies were changed as follows: Rat a-FLAG was diluted at 1:100 and Rabbit a-HA at 1:600. In addition, the tissue was incubated in all three primary antibodies (the two noted above plus mouse α-bruchpilot) simultaneously, diluted in 5% goat serum (GS) in PBT 0.5% Triton X-100 in PBS at RT for four hours on a rotator and then for 48 hr at 4°C on a rotator. Following rinses, as outlined in (Aso et al., 2014), tissue was incubated simultaneously in secondary antibodies ATTO647N goat a-rat IgG (1:150; H and L; Rockland # 612-156-120) and CY3 goat a-rabbit (1:1000; Jackson Immuno Research # 111-165-144) for 4 hr at RT and then 2–3 days at 4°, both incubations on a rotator. Following immunohistochemistry, tissue was post-fixed and rinsed, then mounted on coverslips for further processing. Tissue was dehydrated through an ethanol series, then rinsed in xylene before embedding in DPX mounting medium (Electron Microscopy Sciences, Hatfield, PA). Embedded tissue was dried for two days before imaging.

Images were acquired using a Zeiss LSM 710 confocal microscope and a Plan-Apochromat 63x/NA1.4 oil immersion objective. Frame sizes were 1024 × 1024 pixels with a voxel size of 0.19 x 0.19 × 0.38 micrometers, a zoom factor of 0.8 and one frame average. The reference intensity was ramped throughout the depth of a given sample (reference channel not shown here), but the gain and power for the two data channels (synaptic and membrane) were maintained at constant levels within each sample.

Confocal stacks were viewed and analyzed in the image-viewing software ‘Janelia Workstation’ (S Murphy; K Rokicki; C Bruns; Y Yu; L Foster; E Trautman; D Olbris; T Wolff; A Nern; Y Aso; N Clack; P Davies; S Kravitz; T Safford, unpublished). Since the red channel (synaptotagmin) was weak, the signal was artificially yet uniformly enhanced in the Janelia Workstation so as to be able to see staining in the ellipsoid body, noduli and the protocerebral bridge: this was done only for the P-EN neurons. Additional image processing was not necessary for the E-PG image. The membrane staining (blue) was robust and this channel was not enhanced in the images shown.

As the R37F06 line weakly expresses PB_G2-9.s-FBl3.b-NO₂V.b neurons in addition to the P-EN neurons, we used a second GAL4 line, VT008135, for the synaptotagmin experiments. As confirmed by multicolor stochastic labeling images (data not shown), the only protocerebral bridge neurons targeted by this second line are the P-EN neurons. In the three P-EN brains that did not show obviously higher HA-tagged synaptotagmin in the ellipsoid body, expression was relatively weak in the ellipsoid body but still reasonably strong in the noduli. Over all of the brains, staining in the ellipsoid body was not consistently uniform: in most brains, only some tiles of the ellipsoid body were strongly stained.

The brain and VNC were taken out of 5 to 8 days old female flies and laid on a poly-Lysine coated coverslip. The dissection was realized using the minimum level of illumination possible to avoid spurious activation of CsChrimson. Brains were then continuously perfused in a saline solution with 2 mM CaCl₂, bubbled with carbogen (95% O₂, 5% CO₂) at 60 mL/hour throughout the experiment. Imaging was done on a two-photon scanning microscope (Prairie Technologies, Bruker). The excitation wavelength was 920 nm. P-EN neurons were imaged in the protocerebral bridge and noduli. E-PG neurons were imaged in the gall.

CsChrimson was excited with trains of 2 ms, 590 nm light pulses via an LED shining through the objective. Trains were delivered at 30 Hz. The number of pulses was varied between 1, 5, 10, 20 and 30. Only data obtained from 30-pulse stimulations are shown. Each experimental run consisted of 4 repeats, each approximately 20 s long. Runs were themselves repeated every 2 min (approximately). When present, responses did not desensitize.

For two-color calcium imaging, each volume was collapsed into one maximum intensity image. Regions of interest (ROIs) were then defined over the average of the maximum intensity stacks. For the noduli, ellipses were manually drawn over each side. For the ellipsoid body, ellipses were drawn around the circumference of the P-EN or E-PG neurons and equi-angularly segmented into 16 regions. For the protocerebral bridge, the red and green channels were combined, and 18 polygonal regions of interest were drawn over the glomeruli. 1024 × 1024 pixel stacks consisting of 25–30 planes, each 2 μm apart, were obtained at ~50 mW of power at the end of each bridge experiment to facilitate glomeruli identification.

The stacks were then spatially smoothed using a two pixel wide Gaussian filter, and the mean fluorescence was calculated over each ROI for each maximum intensity image. The lowest 10% ROI means were computed, and all of the ROI means were then divided by the mean of these lowest values and filtered (Savitsky-Golay, third order polynomial) over 11 time steps to match the time scale of the behavior, thereby creating a final ΔF/F.

ΔF/F₀ was calculated for the functional connectivity as follows. F₀ was defined as the average signal before the stimulation. Regions of interest (ROI) were obtained by calculating the average projection of the movie and clustering pixels of the projection by a k-means algorithm between ROI and non-ROI pixels. It's worth noting that the selection method relies only on average intensity and not activity. This is because we want to use the same detection method for responsive and non-responsive runs. This also relies on selecting fields of view as unambiguously containing the neuron of interest — and only the neuron of interest — during the experiment.

To test if the red channel was leaking into the green, or vice versa, the raw fluorescence of the noduli ROIs was compared between the red and green channels in flies that expressed GCaMP6f in the P-ENs and jRGECO1a in the E-PG neurons (Figure 8—figure supplement 1). No clear correlation was seen between the two. Additionally, the noduli were clearly visible in the green channel and nonexistent in the red, while the outer ellipsoid body was clear in the E-PG expressing red channel but not in the green. ROIs drawn around the noduli showed large fluorescence changes in the green and small responses in the red, while an ROI drawn in a region that overlapped the ellipsoid body E-PG processes showed large responses in the red and only small fluctuations in the green.

The population vector average was calculating using the circ_mean function from the Circular Statistics Toolbox (See Data analysis, statistical methods below). For the ellipsoid body, 16 equiangular bins were used, between 0 and 2π, corresponding to the ROIs. For the protocerebral bridge, eight equiangular bins were used, again between 0 and 2π, corresponding to the eight glomeruli in which either the P-EN or the E-PG neurons arborize. The corresponding angle was then multiplied by 8/ (2π) to convert it back into an indicator of the number of the glomerulus.

For protocerebral bridge analysis, changes in the red and green fluorescence transients across the 18 ROIs were sorted into bins according to the rotational velocity at the time of imaging. The right bridge E-PG peak was then used for aligning both the left bridge E-PG signal and the P-EN signal on both sides. That is, for each frame, we drew ROIs corresponding to the estimated boundaries of the 18 bridge glomeruli (see Calculation of fluorescence changes over ROIs), circularly shifted the right ROI fluorescence transients around the nine rightmost protocerebral bridge glomeruli to place the right protocerebral bridge bump peak at glomerulus 14, and circularly shifted the left E-PG bump and the two P-EN bumps by the same offset around their corresponding protocerebral bridge halves. As the P-ENs only arborize in the outer 16 glomeruli and the E-PGs only arborize in the inner 16 glomeruli, the ‘empty’ glomeruli were skipped when shifting the ΔF/Fs for alignment. The population vector average of the mean P-EN and E-PG bump on each side was calculated, and the difference found. We also compared the peaks of the E-PG and P-EN traces, and the mean of the peak and PVA differences for all individual pairs of traces to confirm that the trends were consistent regardless of the statistic that was used. Bump amplitudes and half widths were found for each time point and then averaged.

Further, we confirmed that activity offsets were consistent regardless of which indicator combination labeled the two neural populations, arguing against the lag being merely a result of indicator kinetics (Figure 8G). Finally, the R37F06 line also contains PB_G2-9.s-FBl3.b-NO₂V.b neurons, and so we also compared offsets using a second GAL4 line, VT008135, which only expresses in the P-EN neurons in the bridge. The results were qualitatively the same and quantitatively similar (Figure 8—figure supplement 2).

For the ellipsoid body experiments, the data were binned as described above with the exception that 16 equiangular ROIs were now used. The peak of the E-PG ROI was used for alignment, and the ROIs were circularly shifted to place the peak at position 8 (out of 16). The P-EN ROIs were shifted by the same amount to preserve the relative offset. All statistics were then calculated as described above for the protocerebral bridge. We again compared the two color directions and found that, while clear numerical discrepancies existed, all qualitative trends were the same (Figure 9—figure supplement 2).

We used MATLAB (MathWorks, Inc., Natick, MA) and the Circular Statistics Toolbox (Berens, 2009) for data analysis. All errors and error bars shown are standard deviation (SD). No statistical methods were used to predetermine sample size. Reported sample sizes refer to the number of flies throughout. One-way ANOVA was used for statistical comparisons between three or more groups, Student’s t-test was used to compare differences from zero or between two sets of paired data. Two-way ANOVA was used to compare different experimental groups across conditions.

Our model consists of three populations of neurons corresponding to the E-PG neurons, the P-EN neurons for the left side and the P-EN neurons for the right side. We assume that the E-PG neurons make excitatory synapses onto the P-EN neurons and that the P-EN neurons, in turn, make excitatory synapses onto the E-PG neurons. Finally, we assume global inhibition, a necessary requirement for obtaining localized bumps of activity. A third type of neuron implicitly mediates this interaction from the E-PG neurons to the P-EN neurons. The left and right P-EN populations contain nine neurons each (one neuron per protocerebral bridge glomerulus). Based on anatomical estimates (Wolff et al., 2015), the E-PG population contains three neurons per protocerebral bridge glomerulus, and thus a total of 54 neurons (27 neurons per protocerebral bridge side). Note that some uncertainties remain regarding the topology of the circuit around the ventral side of the ellipsoid body and the medial and lateral-most protocerebral bridge glomeruli. The P-EN neurons are thought not to arborize in the most medial protocerebral bridge glomeruli, and the E-PG neurons are thought to skip the outermost protocerebral bridge glomeruli (although there is a class of E-PG neuron, the PB_G9.b-EB.P.s-ga-t.b, that may help fill this gap [Wolff et al., 2015]). Thus, it is still not entirely clear how the recurrent loop is closed all the way around the ring, something that we simplify in our model by assuming E-PG and P-EN neurons for each of the protocerebral bridge glomeruli.

Neural activity in each of the populations was modeled as a firing rate: ${\displaystyle E({\theta }_i,t)}$ for the E-PG neurons connecting to the left protocerebral bridge, ${\displaystyle E({\theta }_j,t)}$ for the E-PG neurons connecting to the right protocerebral bridge, ${\displaystyle P_l({\theta }_i,t)}$ for the left P-EN neurons and ${\displaystyle P_r({\theta }_i,t)}$ for the right P-EN neurons. The firing rates obeyed the following equations:

where ${\displaystyle \tau =80\text{ms}}$ is the time constant of the E-PG neurons, ${\displaystyle {\tau }_p=65\text{ms}}$ is the time constant of the P-EN neurons, ${\displaystyle \alpha =10}$, and ${\displaystyle \beta =25.{\theta }_{N(i)}}$ is the angular position of the P-EN neurons that project to the E-PG neuron at position ${\displaystyle {\theta }_i.N_k^l}$ and ${\displaystyle N_k^r}$ are the sets of indices for the E-PG neurons that share a protocerebral bridge glomerulus with, respectively, the left or right P-EN neuron at position ${\displaystyle {\theta }_k.K_l({\theta }_i-{\theta }_{N(i)})}$ and ${\displaystyle K_r({\theta }_i-{\theta }_{N(j)})}$ are kernels that describe overlapping connectivity between the E-PG and P-EN populations in the ellipsoid body: ${\displaystyle K_l(\theta )=\frac{f(\theta |0,\kappa )}{2}+f(\theta |{35}^{\circ },\kappa )}$ and ${\displaystyle K_r(\theta )=\frac{f(\theta |0,\kappa )}{2}+f(\theta |-{35}^{\circ },\kappa )}$

where ${\displaystyle f(\theta |\mathrm{\Delta },\kappa )}$ are von Mises distribution functions with ${\displaystyle \kappa =12}$:

The inputs ${\displaystyle v_{+}}$ and ${\displaystyle v_{-}}$ are rectified velocities: ${\displaystyle v_{+}=[v{]}_{+}}$ and ${\displaystyle v_{-}=[-v{]}_{+}}$.

The connectivity matrix of the circuit, corresponding to the previous equations, is shown in Figure 10—figure supplement 1A. The time constants of the dynamics, ${\displaystyle \tau }$ and ${\displaystyle \tau {}_p}$, were chosen to account for the experimentally observed phase shifts in the ellipsoid body and the protocerebral bridge. When a population receives a moving bump as its input, it responds with a spatially shifted moving bump. This phase shift is generally close to ${\displaystyle \tau v}$, where ${\displaystyle \tau }$ is the time constant of the neural population and ${\displaystyle v}$ is the angular velocity of the input. We see that this phase shift is proportional to the angular velocity. Moreover, the angular velocity at which the circuit can no longer track an input velocity and saturates is set by the total time constant and the structural phase shift of the circuit ${\displaystyle \mathrm{△}}$ as follows: ${\displaystyle {\nu }_{sat}=\mathrm{\Delta }/(\tau +{\tau }_{\rho })}$. Thus, ${\displaystyle \tau +{\tau }_{\rho }}$ should be lower than 150 ms for the circuit to track velocity inputs of at least 200°/s. The particular values ${\displaystyle \tau }$ and ${\displaystyle {\tau }_{\rho }}$ were then chosen to obtain the slopes of the phase shifts as a function of the input velocity (Figure 10D).

All simulations were performed with the python programming language, and all code is available at https://github.com/hrouault/ang_veloc_integr (Rouault, 2017), with a copy archived at https://github.com/elifesciences-publications/ang_veloc_integr.

The time auto-correlation functions recorded angular velocities were extracted and fitted by exponentials (Figure 10—figure supplement 2). From these fits, we found a time auto-correlation of 128 ms and a standard deviation of the angular velocity distribution of 54°/s.

In order to test the model over long times and to check for error scaling, we generated artificial angular tracks according to an Ornstein-Uhlenbeck process:

where ${\displaystyle \eta (t)}$ is a Gaussian white noise of variance 1. We took the following parameters for this process: τ = 120 ms, σ = 50°/s.

We input to the network the result of the Ornstein-Uhlenbeck process ${\displaystyle \upsilon (t)}$ shown above. The circuit integrates this input, and the bump position, ${\displaystyle {\theta }_b(t),}$ is recorded over time. We then compared the angular position of the bump with the integrated velocity:

At time scales on the order of the time scale of the circuit, about 200 ms, the difference ${\displaystyle {\theta }_i(t)-{\theta }_b(t)}$ has a finite variance due to the time delay between the input and the actual bump movement. At longer time scales, the behavior of this is diffusive.

The variance of the difference of angles is estimated from the generated trajectories (see Figure 10G) as follows:

And the uncertainty can be evaluated as:

We then fit the difference curve by the following equation:

where we initialized the origin on ${\displaystyle {\theta }_b}$ so that ${\displaystyle {\theta }_b(0)={\theta }_i(0)}$.

We found ${\displaystyle D}$= 1.82×10⁻³ rad²/s and ${\displaystyle {\sigma }_0^2}$= 1.27×10⁻³ rad².

We sought to evaluate the linearity of the angular velocity integration for our model (Figure 10—figure supplement 1B) as a function of the main parameters ${\displaystyle \alpha }$ and ${\displaystyle \beta }$. When varying these parameters, we computed a velocity input-output curve similar to the one displayed in Figure 10E. We then computed the slope of this curve for low velocities, ${\displaystyle {\gamma }_{\mathrm{l}\mathrm{i}\mathrm{n}}}$, in the linear regime. We also computed the ratio of the input and output velocities at the onset of the saturation, ${\displaystyle {\gamma }_{\mathrm{s}\mathrm{a}\mathrm{t}}}$. Our linearity values are expressed as follows:

By definition, if the integration is perfectly linear, ${\displaystyle L}$ should be equal to 1. Note that this definition is correct because we are not varying ${\displaystyle \tau }$, ${\displaystyle {\tau }_p}$ or ${\displaystyle \mathrm{\Delta }}$, and hence the value of the bump velocity at saturation does not vary.

All trials were 2 min long and were performed in the dark. The E-PG neurons were imaged with a wavelength of 930 nm. The line VT025957 was used to image these neurons due to its strong GCaMP expression. The first three trials occurred at room temperature. Heated saline was then perfused across the brain until a temperature of 30°C was reached, as measured by a thermocouple placed near the head capsule. The fly was then shown successive 30 s bouts of clockwise and counterclockwise sine gratings for 5 min to evoke rotational optomotor responses, and the temperature was stabilized at 31°C. We used this 5 min period of turning at the restrictive temperature to accelerate depletion of the pool of P-EN synaptic vesicles. Nine more trials were then performed in the dark. Finally, the perfusion was stopped and the bath was allowed to cool down to 26°C. After waiting a further 10 min, six more trials were performed. The fly’s behavior was variable in the recovery experiments, with some flies continuing to run, some not moving at all, and many gradations in between. Further, the bump amplitude did not recover across all flies. We therefore decided not to include this data. Statistics for walking performance during imaging are available as part of Supplementary file 2.

Significance testing between 31°C distributions (shown in Figure 11D) was performed via bootstrapping. For each pair of datasets, the data of the two pairs were combined. Two new pairs, each with the same number of samples as the original pairs were then created from the combined data via random sampling with replacement. The difference of the means of the two new pairs was then calculated. This procedure was repeated 10,000 times to create a distribution of differences. The difference of the means of the original dataset was then compared to this distribution.

When comparing the change in heading to the change in PVA position for turns, we first defined turns as continuous periods where the rotational speed exceeded 15°/s. We then only considered turns where the PVA strength exceeded a certain threshold. This threshold was defined as follows: for the room temperature cases, where the flies primarily rotate on the ball without much forward walking, we expect the E-PG Ca²⁺ activity to roughly scale with rotational speed. As flies exhibits roughly Gaussian distributions of rotational velocities, we would therefore expect the Ca²⁺ activity to exhibit a half-Gaussian distribution over the course of the experiment. The calculated PVA strength will depend on this activity as follows: At low E-PG activity, the noise of the imaging will dominate the signal, leading to a range of low, but non-zero PVA strengths. During periods of high activity, the signal will dominate, leading to a clear bump and a high PVA strength. Thus, we would expect the PVA strength distribution to be peaked, with the values on the low side of the peak being dictated by noise and the values on the high side of the peak coming from the activity itself. We therefore chose a PVA strength threshold, 0.025, that reliably matched or exceeded the peak of the half-Gaussian distribution seen at higher PVA strengths, attributing values above that to the E-PG activity and values below that to the noise of the system.

Once we had selected turns where the PVA strength exceeded the threshold throughout the turn, we then calculated the change in heading and the change in PVA position over the course of the turn and plotted these values against one another. We fit the data with a linear regression model (using the fitlm function in MATLAB) and extracted the slopes and R² values.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We thank Yi Sun for advice on two-color calcium imaging, Martin Peek, Igor Negrashov and Bill Biddle for help in designing the fly holder for electrophysiology, Adam Taylor and David Ackerman for WaveSurfer development, Lakshmi Ramasamy for help with LabView programming, Scarlett Coffman, Karen Hibbard, and Todd Laverty for fly husbandry, Geoffrey Meissner and the Janelia FlyLight Project Team for brain dissections, histology and confocal imaging for the presynaptic marker experiments, Ellie Sterne for help searching the VT fly collection, Saba Ali, Jack McCarty, Arlo Sheridan, Claire Peterson, and Stanley Tran for useful discussions about the anatomy and structural connectivity of E-PG and P-EN neurons. We thank Ed Rogers and Michael Reiser for generously sharing unpublished fly lines for perturbation experiments. Sung Soo Kim, Hannah Haberkern, and Chuntao Dan provided experimental advice. Emily Nielson created the animation for Video 1. We are grateful to Glenn Turner, Michael Reiser, Eugenia Chiappe, Toshi Hige, Alice Robie, Eyal Gruntman, TJ Florence and members of Vivek's lab for useful discussions, and Arseny Finkelstein, Sandro Romani, and Lorenzo Fontolan for feedback on the manuscript. This work was supported by the Howard Hughes Medical Institute.

1. Received: November 21, 2016
2. Accepted: April 11, 2017
3. Version of Record published: May 22, 2017 (version 1)
4. Version of Record updated: June 6, 2017 (version 2)

© 2017, Turner-Evans et al.

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