Ring attractor dynamics in the Drosophila central brain
Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal’s heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; our study provides physiological evidence of their existence and functional architecture.
studies of neural circuits near the sensory periphery have produced deep mechanistic insights into circuit functions (1, 2). However, it has been more challenging to understand circuit functions in central brain regions dominated by recurrent networks,which often produce complex neural activity patterns.These dynamics play a major role in shaping cognitive functions (3–7), such as the maintenance of head- ing information during navigation (8–10). A head- ing representation must be unique (because an animal can face only one direction at a given time) and persistent (to allow an animal to keep its bearings in darkness), yet must allow updating that matches the magnitude and speed of heading changes expected from the animal’s movements. Theoretically, this can be accomplished by ring attractor networks (11–14), wherein the posi- tion of a localized subset of active neurons in a topological ring represents the animal’sheading direction. However, whether the brain uses these hypothesized networks is still unknown (8, 15). A recent study reported that a population of neurons, called E-PG neurons (Fig. 1, C and D; see supplementary materials for nomenclature), in the Drosophila melanogaster ellipsoid body (EB) appears to use bump-like neural activity dy- namics to represent the animal’s heading in visual environments and in darkness (16, 17). Here, we establish essential properties of the network that enables this representation.
We first determined whether the E-PG popu- lation activity bump tracks the fly’s heading direc- tion relative to its visual surroundings during tethered flight (Fig. 1 and fig. S1). We used two- photon imaging with the genetically encoded cal- cium indicator GCaMP6f (18)torecorddendritic calcium activity of the entire E-PG population in the EB while the fly was flying in a virtual-reality LED arena. The azimuthal velocity of the visual scene was proportional to the fly’s yaw velocity (Fig.1,AandB).Aswithwalkingflies(16), E-PG population activity during flight was organized into a single bump, whether the visual scene contained a single bar (fig. S1B) or a more complex pattern(Fig.1G).Theactivitybumpcloselytracked the fly’s heading in flight (Fig. 1K) and persisted in darkness (Fig. 1H). However, unlike in walking, the activity bump seldom tracked the fly’s motor actions in darkness (Fig. 1, H and K, and fig. S1C), potentially because tethering deprives the fly of nor- mal sensory feedback about its rotational move- ments from its halteres (19). Although the location of the activity bump eventually drifted in some flies, the bump’s movement was, on average, uncorrelated to the animal’s turning move- ments in darkness (Fig. 1K). These findings suggest that the representation of heading in the E-PG population has intact, visually driven dynamics as well as persistence, but is largely uncoupled from updating by self-motion cues during tethered flight.
To test whether the fly’s compass network enforces a unique bump within the EB, we took advantage of the relative persistence of the visually evoked activity bump in darkness, and asked wheth- er this bump could coexist with an “artificial” bump of activity. We used localized optogenetic stimula- tion to create artificial activity bumps in different locations within the E-PG population. Using a transgenic fly line in which E-PG neurons co- expressed CsChrimson (20)andGCaMP6f,weused alternating two-photon laser scan lines of excita- tion (higher laser intensity) and imaging (normal laser intensity) to monitor changes in E-PG pop- ulation dynamics in response to an optogeneti- cally created spot of local activity (Fig. 2, A and B, and fig. S2, A and B). By varying the intensity of stimulation light delivered to the target location, we could create bumps of increased calcium ac- tivity(Fig.2,CtoF,andmovieS1).Asthenewbump formed, activity at the previous location began to decline and eventually disappeared (Fig. 2D) without significantly perturbing the fly’sbehavior (but see fig. S2E). When the optogenetic excitation was terminated, the amplitude of the artificially created bump settled at levels typically evoked by sensory stimuli and did not disappear; it either stayed in the induced location for several sec- onds (fig. S2F) or slowly drifted away (see below) (Fig. 3).
The bump’suniquenessmayarisethroughei- ther recurrent mutual suppression or an indirect mechanism whereby strong bump activity in the EB functionally inhibits feedforward sensory in- puts to other E-PG neurons. To discriminate be- tween these alternatives, we simultaneously excited two locations on the EB ring. A reference location was excited at a fixed laser power, and a second, spatially offset location was excited at increasing levels of laser power (fig. S2G and movies S3 to S5). We could always suppress the reference bump by increasing laser power at the second location above a certain threshold, consistent with mutual suppression.
Recurrent suppression can ensure a unique activity bump through a simple winner-take-all (WTA) circuit (fig. S3A). However, an animal’s representation of its angular orientation should favor more continuous updates based on turning actions. Such gradual, ordered drift to nearby locations would be more consistent with contin- uous, or ring, attractor models (fig. S3, B to D). We therefore examined changes in the location of an artificially created bump after the stabili- zation of its peak activity at the “natural” level. The experiments were performed in darkness to untether the bump from any potentially lingering visual input (Fig. 3). If EB dynamics were driven by a WTA network, bumps would be expected to disappear at times and to jump to random distant locations (fig. S3E). In contrast, the bump drifted gradually around the EB (Fig. 3, B and D, and movie S6); this finding suggests that the fly’s heading representation is updated through functionally excitatory interactions between neigh- boring E-PG neurons, consistent with a ring attractor model. These observations together rule out the possibility that network dynamics in darkness result purely from cell-intrinsic me- chanisms (21, 22) or slowly decaying visual input. Most important, direct manipulation of E-PG neuron activity changed the network state, which implies that E-PG neurons do not merely mirror dynamics occurring in a different circuit, but are themselves an important component of the ring attractor (23).
We next aimed to dissect the effective con- nectivity pattern underlying ring attractor dy- namics in the E-PG population. A wide range of network structures can, in principle, implement ring attractors (11, 13, 14, 24, 25). We focused our efforts to a model space between two extreme net- work architectures that are analytically solvable: (i) a“global model” based on global cosine-shaped interactions (fig. S3B) (11, 13, 26) and (ii) a “local model” based on relatively local excitatory inter- actions (fig. S3D and supplementary text) (24, 27). Under constraints of a fixed bump width of 90° to match physiological observations (Fig. 1J) and an assumption of effectively excitatory visual in- put without any negative bias, both models could explain the basic properties of bump dynamics, including its uniqueness and its persistence in darkness. We therefore probed the network’sre- sponse to more artificial conditions, such as abrupt visual stimulus shifts.
We first examined experimentally how the E-PG population responded to unnatural, abrupt visual shifts. Depending on the distance of the shift, the E-PG bump either“flowed” continuously (shorter shiftdistances;Fig.4,AandC,andmoviesS7and S8) or “jumped” to the new location (longer shift distances; Fig. 4, B and C, and movie S9) (16). In simulations, both models predicted a mixture of jump and flow responses, depending on the strength and width of the abruptly shifting visual input (Fig. 4D, fig. S4A, and supplementary text). For example, weak wide input induced flows and strong narrow input evoked jumps (Fig. 4D). How- ever, the jump-flow balance predicted by the two models differed and was more consistent with the local model in several aspects (Fig. 4D and fig. S4A). First, the visual input strength we in- ferred from normal conditions was much weaker than required by the global model for bump jumps (fig. S1D). Second, the global model required a much-wider-than-normal range of visual input strengthstoexplainjumpsatmultipledistances (Fig. 4D, fig. S1D, and fig. S4A). Third, using pa- rameters consistent with the rest of our findings, we could reproduce the jump-flow ratio observed in Fig. 4C with the local model but not with the global model (fig. S4B).
To obtain more concrete evidence, we compared model predictions to experimentally observed bump dynamics, under conditions in which input strength, polarity, and shift distance were control- led through optogenetic stimulation. To simulate moderate and large input shift distances, we sequentially stimulated two small regions in the EB— each with an angular width of 22.5°—separated by either 90° or 180° (Fig. 4, E to G, movie S10, and fig. S4, C to E). We then varied the stimulation laser power to detect the threshold required for the bump to jump (Fig. 4E). The laser power required to elicit a jump was not significantly different be- tween the two different shift distances, favoring the local model (Fig. 4F). We then inferred the strength of input to the network by comparing the amplitude of the optogenetically evoked bump to natural bump amplitudes in darkness. The opto- genetic input strength required to induce jumps was smaller than the global model’s prediction but matched that of the local model (Fig. 4G) and the range of the inferred visual input strength under normal conditions (fig. S1D, fig. S4, D and E, and movie S11). Finally, when we tested inter- mediate models that lie between the extremes of the local and global models (fig. S4, H and I, and supplementary text), we found that any model that exhibited the observed jumps in response to a weak 22.5°-wide input had narrow connectivity profiles (fig. S4I). All these observations were once again consistent with the local model.
In mammals, heading representations are thought to be distributed across multiple neural populations and multiple brain areas (8). In Drosophila as well, the compass system likely involves multiple cell types, including neurons in the protocerebral bridge (PB) (17, 23). Further, occasional changes observed in the dynamics suggest network modu- lation by other factors not yet known. For exam- ple, we sometimes observed sudden changes in E-PG dynamics, as when the amplitude of the sensory-evoked activity bump changed depending onwhetherornotthetetheredflywasflying(see supplementary materials) and, occasionally, during flight [population vector average (PVA) amplitude plotsinFig.1,GandH,Fig.4,AandB,andfig. S1B]. Nonetheless, the E-PG population provides a powerful physiological handle on the internal representation of heading (16): a single activity bump moving through topographically arranged neurons. The experimental approach this enabled provides one avenue for investigating which of multiple populations are key circuit components of a computation and which simply read out the results of that computation. We found that the artificial bump created by directly manipulating E-PG population activity displays natural dynam- ics, which indicates that these neurons are a key component of the heading circuit.
Our finding that the uniqueness of the E-PG activity bump is ensured via global competition strengthens the conclusion that this population encodes an abstract internal representation of the fly’sheadingdirection(16). Such abstract repre- sentations permit an animal to untether its actions from the grasp of its immediate sensory environ- ment and thereby confer flexibility in both time and behavioral use. Combining an analysis of arti- ficially induced bump dynamics with theoretical modeling allowed us to interrogate this recurrent circuit architecture. We found that the effective network connectivity profile was consistent with ring attractor models characterized by narrow local excitation and flat long-range inhibition. This neural circuit motif of local excitation and long-range inhibition is ubiquitous across many brain areas and across animal taxa (28–31). Such observations support the idea that common circuit motifs might be evolutionarily adapted to serve as crucial building blocks of cognitive function.
Fig. 1. E-PG neurons encode body orientation relative to the visual world during tethered closed-loop flight. (A) Setup schematic. (B)Close-upof tethered flying fly. (C)Centralcomplex.(D) Dendrites of each E-PG neuron innervate wedge-shaped segment of EB; axons project to corresponding glomeruli in PB and Gall. (E) Averaged calcium image of dendritic processes of entire E-PG population segmented into 16 regions of interest (ROIs). (F) Position (PVA direction) and strength (PVA amplitude) of bump obtained by summation of 16 vectors whose lengths represent magnitude of fluorescence transients ( DF/F0). (G) GCaMP6f fluorescence transients in E-PG dendrites during tethered flight in complex visual scene. Top: Visual pattern at sample time points. Second row: Sample frames of calcium imaging.Third row: DF/F0 of 16 ROIs. Grayscale band denotes PVA amplitude; red line is PVA estimate. Fourth row: PVA estimate and heading (blue). Bottom: Same as fourth row, but unwrapped. (H) Fluorescence transients in darkness. (I)Numberofactivity bumpsinE-PGpopulationacrossflies(n = 10) for three visual conditions. Each dot with vertical line indicates mean± SEM for each fly. Population mean ±SEMisshownatleftofeachscatterplot.(J) Bump width measured by full width at half maximum. (K) Correlation between estimated bump position and heading. (L) Angular offset between PVA estimate and scene orientation. Whisker plots, mean ± circular SD.
Fig. 2. E-PG neurons compete by mutually suppressing each other through recurrent connections. (A) Schematic of simultaneous calcium imaging and localized optogenetic stimulation. (B) Analysis procedure for collected images. (C) Top: Temporal profile of two-photon optogenetic stimulation. Bottom: Three sample frames (smoothed with Gaussian filter). Yellow rectangle with arrow, stimulus OFF; red rectangle with arrow, stimulus ON. (D) Time course of calcium dynamics from example fly (left) and population (right). Gray background, optogenetic stimulation period; gray lines, individual trials (left) or flies (right). Top: Mean F of stimulated ROIs. Bottom: Mean of the four most active ROIs outside optogenetically stimulated area before stimulation. Thick colored lines and colored shaded area denote mean and SEM, respectively. (See fig. S2C for control experiment.) (E) Distribution of fluorescence ratio during and before stimulation. P < 0.001, Wilcoxon rank sum test between stimulated (red) and outside stimulation (blue) areas. (See fig. S2D for control experiment.) (F) Suppression by optogenetic stimulation. The x axis indicates distance from stimulation position to existing bump; P <0.001,t test for each distance. Limited sample size prevented a statistical test for p/8.
Fig. 3. Drift of the ac- tivity bump. (A)Sample frames. Same convention as Fig. 2C. (See movie S6.) (B)Temporalevolution of bump position (PVA) over time. Gray background denotes stimulation period. Top: Original bump positions of individual trials (colored thin lines are PVA estimates). Second row: Distance between bump and stimulation position. Red line and shade denote mean± SEM. Bottom: Population mean ± SEM (red) across flies (gray lines). (C) Same as (B), without CsChrimson. (D) Distribution of bump drift distances after the end of optogenetic stimulation. Colored lines represent different conditions. P = 0.324 between gray and blue, P < 0.0001 between blue and red, P < 0.0001 between gray and red; two-sample Kolmogorov-Smirnov tests without multiple-comparisons correction. Distributions are skewed toward short drift distances. Inset shows fraction of trials with drifting bump in each fly (P = 0.0008, t test compared to 0.5).
Fig. 4. Probing the connectivity profile of the ring attractor network. (A) Example of bump“flow” in response to abrupt shift of vertical bar. Same convention as Fig. 1G. Red dots are bump positions estimated from Bayesian sampling method. (B) Bump “jump.” (C) Jump probability increases with distance of visual input shift. Red line and shading denote mean ± SEM. (D) Input-response phase diagrams. Top: Response of local model (fig. S3D) to various input widths, strengths, and abrupt shift distances. Bottom: Global model (fig. S3B). Note that the y axis increments are different between the two models. Red lines denote input strength for bump jump with narrow input, which is constant for the local model and increases with shift distances for the global model. (E) Schematics of stimulation protocol to detect the threshold input strength for bump jump in response to narrow (22.5°) input. Two 22.5° areas were sequentially stimulated. (F) Laser power required to make bump jump from the first stimulation position (1 or 2) to a fixed second stimulation position (A or B). P = 0.102, paired t test. (G) Input strength, estimated by normalized bump amplitude, required for bump jump from fixed first stimulation position to second stimulation position. Red dashed line denotes simulated threshold of the local model. Solid dots are trials with first stimulation at position 1; open dots are trials with first stimulation at position 2.
果蝇中央大脑中的环吸引子动力学。
环形吸引子是一类周期性的网络，被假定为头朝向的代表。这种网络结构被示意为一个神经元环，其连通性取决于其朝向偏好，可以维持bump-like的活动模式，模式位置可以通过沿任一转弯方向的连续移动来更新。我们最近发现一群果蝇神经元通过bump-like的活动动态来代表动物的前进方向。我们将朝向固定飞行的果蝇中的双光子钙成像与光遗传学相结合，以人工的方式覆盖了现有的种群表征，然后由具有自然的动力学的回路加以维持。具有局部激励和全局抑制的网络将强制执行这种唯一且持久的朝向代表。环吸引子网络在理论研究中早已被引用；我们的研究为它们的存在和功能结构提供了生理学证据。
对感觉外围附近神经回路的研究对回路功能产生了深刻的机械洞察力（1、2）。然而，要了解被周期性网络控制的中枢大脑区域的回路功能却更具挑战性，这些网络经常产生复杂的神经活动模式。这些动力学在塑造认知功能中起着重要作用（3-7），例如在导航期间维持朝向信息（8–10）。朝向代表必须是唯一的（因为动物在给定的时间只能面对一个方向），并且必须是持久的（允许动物将其方位保持在黑暗中），但必须允许更新，以符合从动物的运动预期朝向变化的规模和速度。从理论上讲，这可以通过环吸引网络（11-14）来实现，其中拓扑环中活动神经元的局部子集的位置代表了动物的头朝向。但是，大脑是否使用这些假设的网络仍是未知的（8、15）。最近的一项研究报告了一种被称为E-PG神经元的一类在黑腹果蝇椭圆体（EB）出现的神经元群体（图1，C和D；有关命名法，请参见补充材料），这种神经元群体使用bump-like神经元活动动力学来代表动物在视觉环境和黑暗环境中前进的朝向（16，17）。在这里，我们建立了启用此表示法的网络的基本属性。
我们首先确定在束缚飞行期间E-PG的群体活动bump是否跟踪果蝇相对于其视觉周围环境的头朝向（图1和图S1）。我们使用带有遗传编码的钙指示剂GCaMP6f（18）的双光子成像技术来记录苍蝇在虚拟现实LED舞台上飞行时EB中整个E-PG种群的树突钙活动。视觉场景的方位角速度与果蝇的偏航速度成正比（图1，A和B）。随着移动的果蝇（16），在飞行过程中E-PG的种群活动被组织为一个单独的bump，无论视觉场景中是否包含单个横条（图S1B）或更复杂的模式（图1G）。活动bump紧密跟踪飞行中的果蝇朝向（图1K），并在黑暗中持续（图1H）。但是，与步行不同，活动bump很少在黑暗中跟踪果蝇的运动行为（图1，H和K，以及图S1C），这可能是因为系绳会剥夺果蝇从露背上对其旋转运动的正常感官反馈（19）。尽管活动颠簸的位置最终会在一些苍蝇中漂移，但颠簸的运动平均而言与动物在黑暗中的转弯运动无关（图1K）。这些发现表明，在E-PG群体中朝向的表示具有完整的、视觉驱动的动态以及持久性，但是在束缚飞行过程中，很大程度上不受自运动提示的更新的影响。
为了测试苍蝇的罗盘网络是否在EB内实施了独特的颠簸，我们利用了在黑暗中视觉诱发的颠簸的相对持久性，并询问该颠簸是否可以与“人造”颠簸共存。我们使用局部光遗传学刺激在E-PG群中的不同位置创建了人工活动bump。使用E-PG神经元共表达CsChrimson（20）和GCaMP6f的转基因蝇，使用交替的激发（较高激光强度）和成像（正常激光强度）的双光子激光扫描线监测E-PG群体动态的变化，E-PG的种群动态响应于光遗传学造成的局部活动点（图2，A和B，以及图S2，A和B）。通过改变传递到目标位置的刺激光的强度，我们可以创建增加钙电活性的颠簸（图2，CtoF和电影S1）。当新的bump形成时，先前位置的活动开始下降并最终消失（图2）。 2D），而不会显着干扰果蝇行为（但请参见图S2E）。当光遗传学激发终止时，人工产生的bump的振幅稳定在通常由感觉刺激引起的水平，并且没有消失。它要么在诱导位置停留了几秒钟（图S2F），要么缓慢地漂移了（见图3）（图3）。
颠簸的独特性可能通过周期性的相互抑制或间接机制而出现，间接机制使EB中强烈的颠簸活动在功能上抑制了对其他E-PG神经元的前馈感觉输入。为了区分这些替代方案，我们同时激发了EB环上的两个位置。以固定的激光功率激发参考位置，并以增加的激光功率水平激发第二个空间偏移位置（图S2G和电影S3至S5）。我们总是可以通过将第二位置的激光功率提高到一定阈值以上来抑制参考bump，这与相互抑制相一致。
周期性抑制可以通过简单的赢家通吃（WTA）回路（图S3A）来确保独特的活动bump。但是，动物的角度方位代表应该支持基于转弯动作的更连续的更新。这种渐进有序的向附近位置的漂移将与连续或环形吸引子模型更加一致（图S3，B至D）。因此，我们检查了在“自然”水平上其峰值活动稳定之后，人工创建的bump位置的变化。实验是在黑暗中进行的，以消除任何潜在挥之不去的视觉输入带来的颠簸（图3）。如果EB动态是由WTA网络驱动的，则颠簸有时会消失，并会跳到随机的遥远位置（图S3E）。相反的是，bump在EB周围逐渐漂移（图3，B和D，以及电影S6）。这一发现表明，果蝇的航向代表是通过邻近的E-PG神经元之间的功能性兴奋性相互作用来更新的，这与环形吸引子模型是一致的。这些观察结果共同排除了在黑暗中网络动态纯粹由细胞内在机制（21、22）或缓慢衰减的视觉输入产生的可能性。最重要的是，对E-PG神经元活动的直接操纵改变了网络状态，这意味着E-PG神经元不仅反映了在不同回路中发生的动力学，而且本身也是环吸引子的重要组成部分（23）。
接下来，我们旨在剖析E-PG群体中环吸引子动力学的有效连通模式。原则上，各种各样的网络结构都可以实现环形吸引子（11、13、14、24、25）。我们将工作重点放在了两个可以解析解决的极端网络体系结构之间的模型空间上：（i）基于全局余弦形相互作用的“全局模型”（图S3B）（11、13、26）和（ii） 基于相对局部的刺激相互作用（图S3D和补充文本）的“局部模型”（24、27）。在与生理学观察相符合的固定bump宽度为90°的规定下（图1J），并假设有效的兴奋性视觉输入没有任何负偏差，这两个模型都可以解释bump动力学的基本特性，包括其唯一性及其在黑暗中的持续性。因此，我们针对更人工的条件（例如突然的视觉刺激转移）探究了网络的响应。
我们首先通过实验检查了E-PG群体如何应对不自然的、突然的视觉转变。根据变化的距离，E-PGbump要么连续地“流动”（较短的移动距离；图4，A和C，以及电影S7和S8），要么“跳到”新位置（较长的移动距离；图4，B和B）C和电影S9）（16）。在模拟中，两个模型都根据移动的视觉输入的强度和宽度预测了跳跃和流动响应的混合（图4D，图S4A和补充文字）。例如，较弱的宽输入引起的流动和较强的窄输入引起的跳跃（图4D）。但是，两个模型所预测的跳跃流平衡有所不同，并且在几个方面与局部模型更加一致（图4D和图S4A）。首先，我们从正常条件下推断得出的视觉输入强度要比全局模型对颠簸跳跃所要求的弱得多（图S1D）。其次，全局模型要求视觉输入强度的范围要比正常范围大得多，以便解释在多种距离的跳跃（图4D，图S1D和图S4A）。第三，使用与我们的其他发现一致的参数，我们可以使用局部模型而不是整体模型来再现图4C中观察到的跳流比（图S4B）。
为了获得更多具体证据，我们在通过光遗传学刺激控制输入强度、极性和移动距离的条件下，将模型预测与实验观察到的碰撞动力学进行了比较。为了模拟中等和较大的输入移位距离，我们依次刺激了EB中的两个小区域，每个小区域的角度宽度为22.5°，分开了90°或180°（图4，E至G，电影S10和图S4，C至E）。然后，我们改变刺激激光的功率，以检测bump跳跃所需的阈值（图4E）。引起跳跃所需的激光功率在两个不同的移位距离之间没有显着差异，这支持局部模型（图4F）。然后，我们通过比较光遗传学引起的颠簸的振幅与黑暗中自然颠簸的振幅来推断网络输入的强度。诱发跳跃所需的光遗传输入强度小于全局模型的预测，但与局部模型（图4G）和正常条件下推断的视觉输入强度的范围相匹配（图S1D，图S4， D和E，以及电影S11）。最后，当我们测试介于极端的局部模型和全局模型（图S4，H和I，以及补充文本）之间的中间模型时，我们发现，任何显示出观察到的对弱22.5°宽输入响应的跳跃具有较窄的连接配置文件（图S4I）。所有这些观察结果再次与局部模型一致。
在哺乳动物中，朝向代表被认为分布在多个神经种群和多个脑区中（8）。同样在果蝇中，指南针系统可能涉及多种细胞类型，包括前脑桥（PB）中的神经元（17，23）。此外，在动力学中偶尔观察到的变化表明，网络调制是由其他未知因素引起的。例如，有时我们会观察到E-PG动力学的突然变化，因为感觉诱发的活动颠簸的幅度取决于系绳的果蝇是否飞动（参见补充材料）以及偶尔在飞行过程中发生变化[图中的种群矢量平均值（PVA）幅度图.1，GandH，图4，AandB和图。 S1B]。尽管如此，E-PG群体在航向的内部表示上提供了强大的生理处理（16）：单个活动颠簸穿过拓扑排列的神经元。这种启用的实验方法为研究多个群体中的哪些是计算的关键回路元件以及简单地读出该计算的结果提供了一种途径。我们发现，通过直接操纵E-PG种群活动而产生的人工碰撞显示出自然的动力，这表明这些神经元是航向回路的关键组成部分。
我们的发现通过全局竞争确保了E-PG活动颠簸的唯一性，这一发现进一步证实了这一种群编码果蝇头朝向的抽象内部代表的结论（16）。这种抽象代表使动物可以从其直接的感觉环境的控制中解脱出来，从而在时间和行为使用上都具有灵活性。将人为引起的冲击动力学分析与理论建模相结合，使我们可以研究这种周期回路结构。我们发现有效的网络连接配置与环吸引子模型一致，该模型以狭窄的局部刺激和平坦的长范围抑制为特征。这种局部刺激和长范围抑制的神经回路膜体在许多脑区和动物类群中普遍存在（28-31）。这样的观察结果支持了这样一种观点，即常见的回路motifs可能在进化上被用作认知功能的重要组成部分。
图1. E-PG神经元在系绳闭环飞行过程中编码相对于视觉世界的身体朝向。 （A）设置原理图。 （B）系绳飞蝇的特写镜头。（C）中央复合体。（D）每个E-PG神经元的树突神经支配EB的楔形节段；轴突投射到PB和Gall的相应肾小球。 （E）将整个E-PG群的树突状过程的平均钙图像分为16个感兴趣区域（ROIs）。 （F）通过对长度为荧光瞬变幅度（▶️F / F0）的16个矢量求和而获得的bump位置（PVA方向）和强度（PVA振幅）。 （G）在复杂的视觉场景中，系留飞行中E-PG树突中的GCaMP6f荧光瞬变。顶部：采样时间点的视觉模式。第二行：钙成像的样本框架。第三行：16个ROI的DF / F0。灰阶表示PVA振幅；红线是PVA估算值。第四行：PVA估算和朝向（蓝色）。底部：与第四行相同，但解开了。（H）在黑暗中的荧光瞬变。 （I）在三种视觉条件下，跨蝇（n=10）的E-PG种群中的活性突触数量。每个带有垂直线的点表示每个蝇的平均值±SEM。总体平均值±SEM显示在每个散点图的最左边。（J）bump宽度通过半高全宽测量（K）估计的颠簸位置与航向之间的相关性。 （L）PVA估计值与场景方向之间的角度偏移。晶须图，平均值±圆形SD。
图2. E-PG神经元通过周期性连接相互抑制而竞争。（A）同时进行钙成像和局部光遗传学刺激的示意图。 （B）收集图像的分析程序。 （C）上图：双光子光遗传学刺激的时间分布。底部：三个样本框（使用高斯滤镜平滑）。带箭头的黄色矩形，关闭刺激；带有箭头的红色矩形，启用刺激。 （D）实例蝇（左）和种群（右）的钙动力学时程。灰色背景，光遗传刺激期；灰线，个别试验（左）或果蝇群体（右）。上图：受刺激的ROI的平均值F。底部：刺激前，在光遗传学刺激区域之外的四个最活跃的ROI的平均值。粗线和彩色阴影区域分别表示平均值和SEM。 （对照实验见图S2C。）（E）刺激期间和刺激之前的荧光比分布。 P <0.001，在受激区域（红色）和外部刺激区域（蓝色）之间进行Wilcoxon秩和检验。 （对照实验见图S2D。）（F）通过光遗传学刺激进行抑制。 x轴表示从刺激位置到现有bump的距离；P <0.001，对每个距离进行t检验。样本量有限，无法进行派/ 8的统计检验。
图3.活动bump·的漂移。（A）样品架。与图2C相同。 （请参见电影S6。）（B）碰撞位置（PVA）随时间的瞬时旋转。灰色背景表示刺激期。顶部：各个试验的原始凹凸位置（彩色细线为PVA估算值）。第二行：bump与刺激位置之间的距离。红线和阴影表示平均值±SEM。下图：果蝇（灰线）的种群平均数±SEM（红色）。 （C）与（B）相同，但没有CsChrimson。 （D）在光遗传学刺激结束后，bump漂移距离的分布。彩色线代表不同的条件。灰色和蓝色之间P = 0.324，蓝色和红色之间P <0.0001，灰色和红色之间P <0.0001；两样本Kolmogorov-Smirnov测试，无需进行多重比较校正。分布偏向较短的漂移距离。插图显示了每只果蝇中移动bump的试验分数（P = 0.0008，t检验为0.5）。
图4.探测环形吸引器网络的连接配置文件。 （A）响应垂直条的突然偏移而引起的bump“流动”的示例。与图1G相同。红点是根据贝叶斯采样方法估算的bump位置。（B）bump跳”。 （C）跳跃概率随视觉输入移位距离的增加而增加。红线和阴影表示平均值±SEM。 （D）输入-响应相位图。顶部：局部模型（图S3D）对各种输入宽度，强度和突变距离的响应。下：全局模型（图S3B）。请注意，两个模型之间的y轴增量不同。红线表示窄输入时bump跳跃的输入强度，这对于局部模型是恒定的，而对于整体模型，其随移位距离而增加。（E）刺激协议的示意图，用于检测响应于狭窄（22.5°）输入的bump跳跃的输入强度阈值。依次刺激两个22.5°区域。 （F）使bump从第一刺激位置（1或2）跳到固定的第二刺激位置（A或B）所需的激光功率。 P = 0.102，配对t检验。 （G）输入强度，由归一化的碰撞幅度估计，从固定的第一刺激位置到第二刺激位置的bump跳跃所需的输入强度。红色虚线表示局部模型的模拟阈值。实心点是在位置1进行第一次刺激的试验；空心点是在位置2处首次刺激的试验。
#脉络
在一项新的研究中，来自美国霍华德-休斯医学研究所的研究人员发现存在于果蝇大脑中间的一个神经元环路（a ring of neurons）起着指南针（compass）的作用，有助这种昆虫知道它在何处，它去过哪里和它将去往哪里。他们解释了他们如何扩展他们在两年前开始的研究，以及他们的发现可能对哺乳动物的内部导航意味着什么。相关研究结果于2017年5月4日在线发表在Science期刊上，论文标题为“Ring attractor dynamics in the Drosophila central brain”。
正如这些研究人员注意到的那样，他们在两年前已发现大约50个神经元在果蝇大脑中间形成一个环路，并且这个神经元环路似乎起着导航的作用。从那之后，他们研究了这个神经元环路如何可能有助这种昆虫在环境中追踪其行踪。
为此，这些研究人员将果蝇固定在一根金属棒上，这根金属棒让它们呆在原处。他们随后在它们周围播放虚拟现实场景，模拟在它们的自然环境中的运动。当果蝇扇动翅膀试图在这种模拟的场景中飞行时，他们记录了这个神经元环路中的神经活性。他们发现在这个神经元环路中，单个神经元簇集会依据果蝇试图飞行的方向放电。
这些研究人员随后对这个神经元环路中的一些神经元进行基因修饰，从而使得当接受光线照射时，这些神经元会被激活。这允许他们操纵这些果蝇接受到的关于它们的飞行路线的信息。给这些神经元照射光线导致这些果蝇不能够在它们的环境中进行自我追踪，这强烈地提示着他们的观点是对的，即这个神经元环路类似于指南针。他们也开展了类似的实验：让这些果蝇在黑暗中飞行，结果发现尽管它们似乎分不清方向，但是并不清楚的是，这是由于他们的干扰，或者仅是因为它们在黑暗中具有较差的导航技巧。
正如这些研究人员指出的那样，他们的研究提供证据证实了这个神经元环路的用途，但是并没有解释它的神经元是如何被激活的，或者果蝇如何接受来自这个神经元环路的信息和利用它辅助导航。他们计划继续开展他们的研究以便观察他们是否能够找到这些问题的答案。