A rotational velocity estimate constructed through visuomotor competition updates the fly’s neural compass

Abstract

Navigating animals continuously integrate velocity signals to update internal representations of their directional heading and spatial location in the environment. How neural circuits combine sensory and motor information to construct these velocity estimates and how these self-motion signals, in turn, update internal representations that support navigational computations are not well understood. Recent work inDrosophilahas identified a neural circuit that performs angular path integration to compute the fly’s head direction, but the nature of the velocity signal is unknown. Here we identify a pair of neurons necessary for angular path integration that encode the fly’s rotational velocity with high accuracy using both visual optic flow and motor information. This estimate of rotational velocity does not rely on a moment-to-moment integration of sensory and motor information. Rather, when visual and motor signals are congruent, these neurons prioritize motor information over visual information, and when the two signals are in conflict, reciprocal inhibition selects either the motor or visual signal. Together, our results suggest that flies update their head direction representation by constructing an estimate of rotational velocity that relies primarily on motor information and only incorporates optic flow signals in specific sensorimotor contexts, such as when the motor signal is absent.

A neural circuit architecture for rapid behavioral flexibility in goal-directed navigation

“…The fly's compass heading is maintained as a single bump of activity that typically moves in concert with the fly's rotations [16]. We assumed that the bump maintains a von Mises activity profile in the EB, and that its responsiveness to the fly's rotations are accurately maintained by a ring attractor network and by neuron types that we did not explicitly model [29,31,47,[57][58][59][60][61]. We focused instead on the dynamics of the HD representation, which, in our model, are determined by a set of plastic synapses from visual ring neurons to compass neurons that evolve over time via an unsupervised learning process [31,34,[62][63][64] (Fig 2b).…”

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

“…And finally, by bringing functional predictions into several connectomic data sets, the analysis of full-brain behavioral circuits with well-defined inputs becomes feasible. There are many unanswered questions about how optic flow connects to navigation control, for example to the increasingly well-studied visual-motion sensitive neurons in the Central Complex (Hulse et al, 2023, 2021; Lyu et al, 2022; Weir and Dickinson, 2015). By having complete brain data sets (Zheng et al, 2018; Dorkenwald et al, 2023), we can pursue these pathways in less explored regions of the brain—following visual information wherever the anatomy reveals it is flowing.…”

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

“…And finally, by bringing functional predictions into several connectomic data sets, the analysis of full-brain behavioral circuits with well-defined inputs becomes feasible. There are many unanswered questions about how optic flow connects to navigation control, for example to the increasingly well-studied visual-motion sensitive neurons in the Central Complex (Hulse et al, 2023(Hulse et al, , 2021Lyu et al, 2022 ;Weir and Dickinson, 2015 ). By having complete brain data sets (Zheng et al, 2018 ;Dorkenwald et al, 2023 ), we can pursue these pathways in less explored regions of the brain-following visual information wherever the anatomy reveals it is flowing.…”