Reciprocal inhibitory connections within a neural network for rotational optic-flow processing

A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity

Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of 'optic-flow' sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.

Survival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system’s processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.

Feedforward Inhibition Conveys Time-Varying Stimulus Information in a Collision Detection Circuit

Feedforward inhibition is ubiquitous as a motif in the organization of neuronal circuits. During sensory information processing, it is traditionally thought to sharpen the responses and temporal tuning of feedforward excitation onto principal neurons. As it often exhibits complex time-varying activation properties, feedforward inhibition could also convey information used by single neurons to implement dendritic computations on sensory stimulus variables. We investigated this possibility in a collision-detecting neuron of the locust optic lobe that receives both feedforward excitation and inhibition. We identified a small population of neurons mediating feedforward inhibition, with wide visual receptive fields and whose responses depend both on the size and speed of moving stimuli. By studying responses to simulated objects approaching on a collision course, we determined that they jointly encode the angular size of expansion of the stimulus. Feedforward excitation, on the other hand, encodes a function of the angular velocity of expansion and the targeted collision-detecting neuron combines these two variables non-linearly in its firing output. Thus, feedforward inhibition actively contributes to the detailed firing-rate time course of this collision-detecting neuron, a feature critical to the appropriate execution of escape behaviors. These results suggest that feedforward inhibition could similarly convey time-varying stimulus information in other neuronal circuits.

Spike Burst Coding of Translatory Optic Flow and Depth from Motion in the Fly Visual System

Many animals use the visual motion generated by traveling straight-the translatory optic flow-to successfully navigate obstacles: near objects appear larger and to move more quickly than distant objects. Flies are expert at navigating cluttered environments, and while their visual processing of rotatory optic flow is understood in exquisite detail, how they process translatory optic flow remains a mystery. We present novel cell types that have local motion receptive fields matched to translation self-motion, the vertical translation (VT) cells. One of these, the VT1 cell, encodes self-motion in the forward-sideslip direction and fires action potentials in spike bursts as well as single spikes. We show that the spike burst coding is size and speed-tuned and is selectively modulated by motion parallax-the relative motion experienced during translation. These properties are spatially organized, so that the cell is most excited by clutter rather than isolated objects. When the fly is presented with a simulation of flying past an elevated object, the spike burst activity is modulated by the height of the object, and the rate of single spikes is unaffected. When the moving object alone is experienced, the cell is weakly driven. Meanwhile, the VT2-3 cells have motion receptive fields matched to the lift axis. In conjunction with previously described horizontal cells, the VT cells have properties well suited to the visual navigation of clutter and to encode the fly's movements along near cardinal axes of thrust, lift, and forward sideslip.

Near-optimal decoding of transient stimuli from coupled neuronal subpopulations

Coupling between sensory neurons impacts their tuning properties and correlations in their responses. How such coupling affects sensory representations and ultimately behavior remains unclear. We investigated the role of neuronal coupling during visual processing using a realistic biophysical model of the vertical system (VS) cell network in the blow fly. These neurons are thought to encode the horizontal rotation axis during rapid free-flight maneuvers. Experimental findings suggest that neurons of the VS are strongly electrically coupled, and that several downstream neurons driving motor responses to ego-rotation receive inputs primarily from a small subset of VS cells. These downstream neurons must decode information about the axis of rotation from a partial readout of the VS population response. To investigate the role of coupling, we simulated the VS response to a variety of rotating visual scenes and computed optimal Bayesian estimates from the relevant subset of VS cells. Our analysis shows that coupling leads to near-optimal estimates from a subpopulation readout. In contrast, coupling between VS cells has no impact on the quality of encoding in the response of the full population. We conclude that coupling at one level of the fly visual system allows for near-optimal decoding from partial information at the subsequent, premotor level. Thus, electrical coupling may provide a general mechanism to achieve near-optimal information transfer from neuronal subpopulations across organisms and modalities.

Subcellular mapping of dendritic activity in optic flow processing neurons

Dendritic integration is a fundamental element of neuronal information processing. So far, few studies have provided a detailed spatial picture of this process, describing the properties of local dendritic activity and its subcellular organization. Here, we used 2-photon calcium imaging in optic flow processing neurons of the fly Calliphora vicina to determine the preferred location and direction of local motion cues for small branchlets throughout the entire dendrite. We found a pronounced retinotopic mapping on both the subcellular and the cell population level. In addition, dendritic branchlets residing in different layers of the neuropil were tuned to distinct directions of motion. Summing the local receptive fields of all dendritic branchlets reproduced the characteristic properties of these neurons' axonal output receptive fields. Our results corroborate the notion that the dendritic morphology of vertical system cells allows them to selectively collect local motion inputs with particular directional preferences from a spatially organized input repertoire, thus forming filters that match global patterns of optic flow. Furthermore, we suggest that the facet arrangement across the fly's eye shapes the subcellular direction tuning to local motion stimuli. These data illustrate a highly structured circuit organization as an efficient way to hard-wire a complex sensory task.

Binocular interactions underlying the classic optomotor responses of flying flies

In response to imposed course deviations, the optomotor reactions of animals reduce motion blur and facilitate the maintenance of stable body posture. In flies, many anatomical and electrophysiological studies suggest that disparate motion cues stimulating the left and right eyes are not processed in isolation but rather are integrated in the brain to produce a cohesive panoramic percept. To investigate the strength of such inter-ocular interactions and their role in compensatory sensory–motor transformations, we utilize a virtual reality flight simulator to record wing and head optomotor reactions by tethered flying flies in response to imposed binocular rotation and monocular front-to-back and back-to-front motion. Within a narrow range of stimulus parameters that generates large contrast insensitive optomotor responses to binocular rotation, we find that responses to monocular front-to-back motion are larger than those to panoramic rotation, but are contrast sensitive. Conversely, responses to monocular back-to-front motion are slower than those to rotation and peak at the lowest tested contrast. Together our results suggest that optomotor responses to binocular rotation result from the influence of non-additive contralateral inhibitory as well as excitatory circuit interactions that serve to confer contrast insensitivity to flight behaviors influenced by rotatory optic flow.

Disentangling the functional consequences of the connectivity between optic-flow processing neurons

Typically, neurons in sensory areas are highly interconnected. Coupling two neurons can synchronize their activity and affect a variety of single-cell properties, such as their stimulus tuning, firing rate or gain. All of these factors must be considered to understand how two neurons should be coupled to optimally process stimuli. We quantified the functional effect of an interaction between two optic-flow processing neurons (Vi and H1) in the fly (Lucilia sericata). Using a generative model, we estimated a uni-directional coupling from H1 to Vi. Especially at a low signal-to-noise ratio (SNR), the coupling strongly improved the information about optic-flow in Vi. We identified two constraints confining the strength of the interaction. First, for weak couplings, Vi benefited from inputs by H1 without a concomitant shift of its stimulus tuning. Second, at both low and high SNR, the coupling strength lay in a range in which the information carried by single spikes is optimal.

Bio-inspired visual ego-rotation sensor for MAVs

Flies are capable of extraordinary flight maneuvers at very high speeds largely due to their highly elaborate visual system. In this work we present a fly-inspired FPGA based sensor system able to visually sense rotations around different body axes, for use on board micro aerial vehicles (MAVs). Rotation sensing is performed analogously to the fly's VS cell network using zero-crossing detection. An additional key feature of our system is the ease of adding new functionalities akin to the different tasks attributed to the fly's lobula plate tangential cell network, such as object avoidance or collision detection. Our implementation consists of a modified eneo SC-MVC01 SmartCam module and a custom built circuit board, weighing less than 200 g and consuming less than 4 W while featuring 57,600 individual two-dimensional elementary motion detectors, a 185° field of view and a frame rate of 350 frames per second. This makes our sensor system compact in terms of size, weight and power requirements for easy incorporation into MAV platforms, while autonomously performing all sensing and processing on-board and in real time.

Neural action fields for optic flow based navigation: a simulation study of the fly lobula plate network

Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its ‘action field’. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly.

Processing of horizontal optic flow in three visual interneurons of the Drosophila brain

Motion vision is essential for navigating through the environment. Due to its genetic amenability, the fruit fly Drosophila has been serving for a lengthy period as a model organism for studying optomotor behavior as elicited by large-field horizontal motion. However, the neurons underlying the control of this behavior have not been studied in Drosophila so far. Here we report the first whole cell recordings from three cells of the horizontal system (HSN, HSE, and HSS) in the lobula plate of Drosophila. All three HS cells are tuned to large-field horizontal motion in a direction-selective way; they become excited by front-to-back motion and inhibited by back-to-front motion in the ipsilateral field of view. The response properties of HS cells such as contrast and velocity dependence are in accordance with the correlation-type model of motion detection. Neurobiotin injection suggests extensive coupling among ipsilateral HS cells and additional coupling to tangential cells that have their dendrites in the contralateral hemisphere of the brain. This connectivity scheme accounts for the complex layout of their receptive fields and explains their sensitivity both to ipsilateral and to contralateral motion. Thus the main response properties of Drosophila HS cells are strikingly similar to the responses of their counterparts in the blowfly Calliphora, although we found substantial differences with respect to their dendritic structure and connectivity. This long-awaited functional characterization of HS cells in Drosophila provides the basis for the future dissection of optomotor behavior and the underlying neural circuitry by combining genetics, physiology, and behavior.

Robust coding of ego-motion in descending neurons of the fly

In many species, motion-sensitive neurons responding to optic flow at higher processing stages are well characterized; however, less is known how this representation of ego-motion is further transformed into an appropriate motor response. Here, we analyzed in the blowfly Calliphora vicina the visuomotor transformation from motion-sensitive neurons in the lobula plate [V2 and vertical system (VS) cells] onto premotor descending neurons [descending neurons of the ocellar and vertical system (DNOVS) cells] feeding into the motor circuit of the fly thoracic ganglion. We found that each of these cells is tuned to rotation of the fly around a particular body axis. Comparing the responses of presynaptic and postsynaptic cells revealed that DNOVS cells have approximately the same tuning widths as V2 and VS cells. However, DNOVS signals cells are less corrupted by fluctuations arising from the spatial structure of the visual input than their presynaptic elements. This leads to a more robust representation of ego-motion at the level of descending neurons. Thus, when moving from lobula plate cells to descending neurons, the selectivity for a particular optic flow remains unaltered, but the robustness of the representation increases.

Local and global motion preferences in descending neurons of the fly

For a moving animal, optic flow is an important source of information about its ego-motion. In flies, the processing of optic flow is performed by motion sensitive tangential cells in the lobula plate. Amongst them, cells of the vertical system (VS cells) have receptive fields with similarities to optic flows generated during rotations around different body axes. Their output signals are further processed by pre-motor descending neurons. Here, we investigate the local motion preferences of two descending neurons called descending neurons of the ocellar and vertical system (DNOVS1 and DNOVS2). Using an LED arena subtending 240° × 95° of visual space, we mapped the receptive fields of DNOVS1 and DNOVS2 as well as those of their presynaptic elements, i.e. VS cells 1–10 and V2. The receptive field of DNOVS1 can be predicted in detail from the receptive fields of those VS cells that are most strongly coupled to the cell. The receptive field of DNOVS2 is a combination of V2 and VS cells receptive fields. Predicting the global motion preferences from the receptive field revealed a linear spatial integration in DNOVS1 and a superlinear spatial integration in DNOVS2. In addition, the superlinear integration of V2 output is necessary for DNOVS2 to differentiate between a roll rotation and a lift translation of the fly.

Sensory integration: neuronal adaptations for robust visual self-motion estimation

Recent studies on the fly visual system have revealed how the morphology of visual interneurons and their lateral electrical connectivity helps overcome a notorious problem in visuomotor control - the ambiguity of local sensor signals.

Nonlinear integration of binocular optic flow by DNOVS2, a descending neuron of the fly

For visual orientation and course stabilization, flies rely heavily on the optic flow perceived by the animal during flight. The processing of optic flow is performed in motion-sensitive tangential cells of the lobula plate, which are well described with respect to their visual response properties and the connectivity among them. However, little is known about the postsynaptic descending neurons, which convey motion information to the motor circuits in the thoracic ganglion. Here we investigate the physiology and connectivity of an identified premotor descending neuron, called DNOVS2 (for descending neuron of the ocellar and vertical system). We find that DNOVS2 is tuned in a supralinear way to rotation around the longitudinal body axis. Experiments involving stimulation of the ipsilateral and the contralateral eye indicate that ipsilateral computation of motion information is modified nonlinearly by motion information from the contralateral eye. Performing double recordings of DNOVS2 and lobula plate tangential cells, we find that DNOVS2 is connected ipsilaterally to a subset of vertical-sensitive cells. From the contralateral eye, DNOVS2 receives input most likely from V2, a heterolateral spiking neuron. This specific neural circuit is sufficient for the tuning of DNOVS2, making it probably an important element in optomotor roll movements of the head and body around the fly's longitudinal axis.