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Ozdil, P. G.

Publications and source records attributed to Ozdil, P. G..

2 recordsLinked to original sources

Centralized brain networks underlie body part coordination during grooming

Animals must coordinate multiple body parts to perform important tasks such as grooming, or locomotion. How this movement synchronization is achieved by the nervous system remains largely unknown. Here, we uncover the neural basis of body part coordination during goal-directed antennal grooming in the fly, Drosophila melanogaster. We find that unilateral or bilateral grooming of one or both antenna, respectively, arises from synchronized movements of the head, antennae, and forelegs. Simulated replay of these body part kinematics in a biomechanical model shows that this coordination makes grooming more efficient by permitting unobstructed, forceful collisions between the foreleg tibiae and antennae. Movements of one body part do not require proprioceptive sensory feedback from the others: neither amputation of the forelegs or antennae, nor immobilization of the head prevented movements of the other unperturbed body parts. By constructing a comprehensive antennal grooming network from the fly brain connectome, we find that centralized interneurons and shared premotor neurons interconnect and thus likely synchronize neck, antennal, and foreleg motor networks. A simulated activation screen of neurons in this network reveals cell classes required for the coordination of antennal movements during unilateral grooming. These cells form two coupled circuit motifs that enable robust body part synchronization: a recurrent excitatory subnetwork that promotes contralateral antennal pitch and broadcast inhibition that suppresses ipsilateral antennal pitch. Similarly centralized controllers may enable the flexible co-recruitment of multiple body parts to subserve a variety of behaviors.

neuroscience↗

NeuroMechFly 2.0, a framework for simulating embodied sensorimotor control in adult Drosophila

Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Until now, such models, including NeuroMechFly for the adult fly, Drosophila melanogaster, have primarily been used to investigate motor control. Far less studied with realistic body models is how the brain and motor systems work together to perform hierarchical sensorimotor control. Here we present NeuroMechFly v2, a framework that expands Drosophila neuromechanical modeling by enabling visual and olfactory sensing, ascending motor feedback, and complex terrains that can be navigated using leg adhesion. We illustrate its capabilities by first constructing biologically inspired locomotor controllers that use ascending motor feedback to perform path integration and head stabilization. Then, we add visual and olfactory sensing to this controller and train it using reinforcement learning to perform a multimodal navigation task in closed loop. Finally, we illustrate more biorealistic modeling in two ways: our model navigates a complex odor plume using a Drosophila odor taxis strategy, and it uses a connectome-constrained visual system network to follow another simulated fly. With this framework, NeuroMechFly can be used to accelerate the discovery of explanatory models of the nervous system and to develop machine learning-based controllers for autonomous artificial agents and robots.

neuroscience↗