A competitive disinhibitory network for robust optic flow processing inDrosophila

Abstract

Many animals rely on optic flow for navigation, using differences in eye image velocity to detect deviations from their intended direction of travel. However, asymmetries in image velocity between the eyes are often overshadowed by strong, symmetric translational optic flow during navigation. Yet, the brain efficiently extracts these asymmetries for course control. While optic flow sensitive-neurons have been found in many animal species, far less is known about the postsynaptic circuits that support such robust optic flow processing. In the flyDrosophila melanogaster, a group of neurons called the horizontal system (HS) are involved in course control during highspeed translation. To understand how HS cells facilitate robust optic flow processing, we identified central networks that connect to HS cells using full brain electron microscopy datasets. These networks comprise three layers: convergent inputs from different, optic flow-sensitive cells, a middle layer with reciprocal, and lateral inhibitory interactions among different interneuron classes, and divergent output projecting to both the ventral nerve cord (equivalent to the vertebrate spinal cord), and to deeper regions of the fly brain. By combining two-photon optical imaging to monitor free calcium dynamics, manipulating GABA receptors and modeling, we found that lateral disinhibition between brain hemispheres enhance the selectivity to rotational visual flow at the output layer of the network. Moreover, asymmetric manipulations of interneurons and their descending outputs induce drifts during highspeed walking, confirming their contribution to steering control. Together, these findings highlight the importance of competitive disinhibition as a critical circuit mechanism for robust processing of optic flow, which likely influences course control and heading perception, both critical functions supporting navigation.

A comprehensive neuroanatomical survey of theDrosophilaLobula Plate Tangential Neurons with predictions for their optic flow sensitivity

“…As it is less clear how to account for the contribution of inhibitory inputs into PMPMs, we do not include them in our estimates, but separately depict their potential contribution in the more detailed plots for each LPT-target neuron (Supplementary File 5). We note that the predicted tuning of uLPTCrns (#23 and #51) and DNp15 (#52), featuring a marked preference for yaw rotation, are in excellent agreement with recent physiological measurements of these neurons (Erginkaya et al, 2023).…”

“…To enable the analysis of circuits downstream of the LPTs, we endeavored to match the cells, one-for-one, between FAFB and Hemibrain, making productive use of both connectome data sets together. Since the Hemibrain contains only a sliver of the LOP, matching neurons relied on their central brain morphology in the Hemibrain and their connectivity (see Methods), in an iterative matching process (coordinated with Erginkaya, et al (2023)). We noted that the LPT axons were in brain regions that were not as thoroughly proofread as others, and so we initiated additional proofreading in the Hemibrain, targeted to our matched LPTs.…”

“…It is known that the VS cells are coupled via gap junctions (Ammer et al, 2022; Farrow et al, 2005; Joesch et al, 2008), which are not detectable in our EM data sets. The lack of connectivity to HS cells does not result from any technical limitation, and instead appears to reflect how specialized they are, not sharing direct connections with other LPT neurons (Erginkaya et al, 2023). (3) Many LPT-like LOP input neurons play prominent roles in this network.…”

“…We think there is a golden opportunity to thoroughly understand this network in Drosophila , where this graph, together with our PMPMs for the LPTs, establishes a roadmap for experimental validation—marked differences from the predicted flow field sensitivity may well be explained via these network connections. A detailed exploration of the HS-cell network (Erginkaya et al, 2023), shows the power of combining connectomic predictions with functional studies of these networks.…”

“…Perhaps the clearest example involves the monocular signals of horizontal-motion-sensitive cells (like HS, H2, etc.) that could be used to encode either yaw rotations or body translations in the equatorial plane; these different body movements could be robustly distinguished by combining inputs from these cells across both eyes (a major theme explored in Erginkaya et al, 2023). To look for these combinations, we investigated the direct synaptic targets of LPT neurons in the central brain.…”