Motor context coordinates visually guided walking inDrosophila

Abstract

Course control is critical for the acquisition of spatial information during exploration and navigation, and it is thought to rely on neural circuits that process locomotive-related multimodal signals. However, which circuits underlie this control, and how multimodal information contributes to the control system are questions poorly understood. We used Virtual Reality to examine the role of self-generated visual signals (visual feedback) on the control of exploratory walking in flies. Exploratory flies display two distinct motor contexts, characterized by low speed and fast rotations, or by high speed and slow rotations, respectively. Flies use visual feedback to control body rotations, but in a motor-context specific manner, primarily when walking at high speed.Different populations of visual motion-sensitive cells estimate body rotations via congruent, multimodal inputs, and drive compensatory rotations. However, their effective contribution to course control is dynamically tuned by a speed-related signal. Our data identifies visual networks with a multimodal circuit mechanism for adaptive course control and suggests models for how visual feedback is combined with internal signals to guide exploratory course control.

Two Brain Pathways Initiate Distinct Forward Walking Programs in Drosophila

“…We calculated angular velocity, translational velocity, distance traveled and total angular rotation (area under the curve of absolute angular velocity) for each trial and then averaged the trials for plotting and statistical analysis of per fly attributes. Straightness or Straightness Index (SI) was a modified version of a previously reported definition (Cruz et al, 2019) and calculated for first 4 s of optogenetic activation for the trial that showed longest distance covered. In order to calculate straightness without any effect of velocity we defined SI as a pure spatial parameter.…”

Neural mechanisms to incorporate visual counterevidence in self motion estimation

“…J. Kim et al, 2017). Here, we recorded calcium responses of five different types of horizontally tuned LPTCs: HS (Hausen, 1976; Schnell et al, 2010), H2 (Cruz et al, 2019; Farrow et al, 2006; Hausen, 1982; Wei et al, 2020), CH (Boergens et al, 2018; Eckert and Dvorak, 1983; Farrow et al, 2006; Wei et al, 2020), Hx (Wasserman et al, 2015), and FD1 (Egelhaaf, 1985; Wei et al, 2020) ( Figure 4g, h ). We imaged the lobula plate dendrites of these cells expressing jGCaMP7b (Dana et al, 2019), which has a relatively low Ca 2+ dissociation constant and is thus suited for detecting hyperpolarization.…”

“…To do so, expected self motion, given the motor commands, should be subtracted from net self motion estimated from sensory inputs (von Holst and Mittelstaedt, 1950). Interestingly, recent studies in Drosophila have reported that optic flow-sensitive LPTCs switch between encoding net and inadvertent self rotation depending on their locomotor modes: In walking flies, optic flow and motor commands act additively in both HS (Cruz et al, 2019; Fujiwara et al, 2022, 2017) and H2 cells (Cruz et al, 2019), such that their response amplitudes are maximal when flies are voluntarily turning and receiving congruent visual feedback, consistent with net rotation encoding. In contrast, in flying flies, visual and motor signals act subtractively, such that HS cells are silent during voluntary turns (Fenk et al, 2021; Kim et al, 2015; A.…”

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

“…We adapted an optomotor paradigm 30 to a highthroughput configuration to test MARGO's ability to deliver a precise closed-loop stimulus with low latency. In this paradigm, an optomotor stimulus consisting of a highcontrast, rotating pinwheel, centered on a fly, is projected on the floor of the arena in which it is walking freely.…”