Biology subjects

Arreguit, J.

Publications and source records attributed to Arreguit, J..

3 recordsLinked to original sources

bioRxiv2024

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↗

bioRxiv2023

FARMS: Framework for Animal and Robot Modeling and Simulation

The apparent ease with which animals move hides the complexity of the neuromechanical systems underlying locomotion. This behavior remains difficult to understand and replicate, motivating sustained interest across biology, neuroscience, robotics, and computer animation. Despite extensive progress, existing approaches often focus on isolated aspects of locomotion, limiting the integration of neural control, biomechanics, and environmental interactions within a unified modeling framework. Bridging biological insight, robotic implementation, and physically based character animation therefore requires shared, extensible computational tools that promote reuse, while supporting physically grounded modeling and control across animals and robots. Our key technical contribution is the development of FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary Python framework that integrates and extends existing open-source software for 3D modeling, experiment design, optimization, and data analysis within a modular and extensible workflow. FARMS lowers the barrier for constructing and simulating whole-body animal and robot models, enabling the definition of skeletal structures, musculature, and neural circuits at multiple levels of abstraction within a unified workflow. FARMS has already been used to model and study locomotor behaviors in a variety of animals and robots, and we demonstrate the frameworks versatility and effectiveness through a series of simulation case studies. We show that FARMS enables the reuse of model components across morphologies of different complexities, while capturing mechanical interactions between the body and diverse environments, including terrestrial and aquatic settings. This allows the study and synthesis of walking, swimming, and locomotor transitions within a single, unified framework.

animal behavior and cognition↗

bioRxiv2021

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Animal behavior emerges from a seamless interaction between neural network dynamics, musculoskeletal properties, and the physical environment. Accessing and understanding the interplay between these intertwined elements requires the development of integrative and morphologically realistic neuromechanical simulations. Until now, there has been no such simulation framework for the widely studied model organism, Drosophila melanogaster. Here we present NeuroMech-Fly, a data-driven model of the adult female fly within a physics-based simulation environment. NeuroMechFly combines a series of independent computational modules including a biomechanical exoskeleton with articulating body parts-legs, halteres, wings, abdominal segments, head, proboscis, and antennae-muscle models, and neural network controllers. To enable illustrative use cases, we first define minimal leg degrees-of-freedom by analyzing real 3D kinematic measurements during real Drosophila walking and grooming. Then, we show how, by replaying these behaviors using NeuroMechFlys biomechanical exoskeleton in its physics-based simulation environment, one can predict otherwise unmeasured torques and contact reaction forces. Finally, we leverage NeuroMechFlys full neuromechanical capacity to discover neural networks and muscle parameters that enable locomotor gaits optimized for speed and stability. Thus, NeuroMechFly represents a powerful testbed for building an understanding of how behaviors emerge from interactions between complex neuromechanical systems and their physical surroundings.

neuroscience↗