Periodic Reporting for period 1 - SIMPLIFLY (Sensorimotor Integration, Motor Planning and Learning In FLY)
Période du rapport: 2023-09-01 au 2025-12-31
Whether it is to adapt to changes of the body or the environmental conditions, or to learn new motor skills, animals must modify their actions to execute tasks accurately in a process known as motor learning. Several models, such as forward and inverse models, and direct learning policy have been proposed for motor adaptation in humans. Through the update of these internal models and policies, the brain is thought to maintain an accurate representation of self-motion for precise motor control. Despite the importance of this neurobiological process for smooth movement control, injury and disease recovery, it remains unclear how accurate self-motion representations update motor commands. For instance, an important component of motor learning concerns the recalibration between sensory signals and internal motor information, and generally involves the generation of an error signal due to the mismatch between the observed and intended movement.
Exploratory animals structure their behavior to maximize gaze stability, thereby facilitating the acquisition of visual and spatial information while minimizing retinal slip. Such gaze control depends on multi-sensory integration, but the circuit mechanisms underlying precise multi-modal calibration during locomotion remain unclear. Partially, This is due to the complexity of the mammalian brain, and the distributed nature of circuits involved in motor control, recruiting several distributed areas of the mammalian brain with unclear specific contribution.The fruit fly, Drosophila melanogaster, provides a unique opportunity for a comprehensive mechanistic understanding of motor learning because of its compact brain and the recently developed whole central nervous system connectomic dataset, both of which facilitates the study of distributed internal representations, and the role of specific brain areas and genetically identified classes of neurons.
Previous work in the Chiappe laboratory as shown that walking flies maximize gaze stability through distinct coordinated head and body motor program. Moreover, gaze stability was improved when self-generated visual feedback was available to the animal and use to tune postural reflexes. We aim to unravel the neural basis of motor adaptation using a population of visual neurons thought to contribute to steering control, and whose activity is strongly modulated by the insect’s ongoing motor programs. We combined 2-photon calcium imaging and a virtual reality environment where we could manipulated the self-motion generated visual feedback available to the animal.
Exploratory animals structure their behavior to maximize gaze stability, thereby facilitating the acquisition of visual and spatial information while minimizing retinal slip. Such gaze control depends on multi-sensory integration, but the circuit mechanisms underlying precise multi-modal calibration during locomotion remain unclear. Partially, This is due to the complexity of the mammalian brain, and the distributed nature of circuits involved in motor control, recruiting several distributed areas of the mammalian brain with unclear specific contribution.The fruit fly, Drosophila melanogaster, provides a unique opportunity for a comprehensive mechanistic understanding of motor learning because of its compact brain and the recently developed whole central nervous system connectomic dataset, both of which facilitates the study of distributed internal representations, and the role of specific brain areas and genetically identified classes of neurons.
Previous work in the Chiappe laboratory as shown that walking flies maximize gaze stability through distinct coordinated head and body motor program. Moreover, gaze stability was improved when self-generated visual feedback was available to the animal and use to tune postural reflexes. We aim to unravel the neural basis of motor adaptation using a population of visual neurons thought to contribute to steering control, and whose activity is strongly modulated by the insect’s ongoing motor programs. We combined 2-photon calcium imaging and a virtual reality environment where we could manipulated the self-motion generated visual feedback available to the animal.
We tested whether flies maintain gaze stability under visual perturbations by immersing them in a virtual world and subjected them to constant rotations of this world. Flies adjusted their velocity to preserve gaze stability, underscoring visuomotor recalibration. To begin to understand the neural underpinnings of such recalibration, we adapted this parading to head-fixed flies walking on a spherical treadmill to record neural activity simultaneously from a population of genetically identified neurons involved in gaze control. The GABAergic bilateral Inferior Posterior Slope (bIPS) cells receive multimodal information from integrative brain regions and the VNC (the insect analogue of the spinal cord), providing an anatomical substrate for calibration. Recordings from bIPS in walking flies showed that they congruently combine retinal and extra-retinal signals. Moreover, this congruent multimodal combination sharpens the neuron’s sensitivity to the body’s translation and rotation. Ongoing experiments are testing the activity of bIPS under visual perturbations to examine recalibration at the level of bIPS activity.
Overall, using a combination of quantitative analysis of behavior, 2- photon optical neural recordings in flies, and targeted manipulations of neural activity, we established a motor adaptation paradigm for D. melanogaster, are characterizing the activity of a neural circuits during visuomotor adaptation and manipulating the contribution of different neurons to the observed adaptation.
Overall, using a combination of quantitative analysis of behavior, 2- photon optical neural recordings in flies, and targeted manipulations of neural activity, we established a motor adaptation paradigm for D. melanogaster, are characterizing the activity of a neural circuits during visuomotor adaptation and manipulating the contribution of different neurons to the observed adaptation.
Together, our data underscores the properties of an integrative inhibitory hub involved in steering during locomotion. Future work leveraging the EM connectomics datasets will test the mechanisms by which bIPS combine and calibrate multi-modal information for gaze control in the context of exploration. Taking advantage of rapid volumetric scanning method, we are interrogating which brain area might be important for re-calibration by conveying error signal.
Overall, this project proposal allow a better understanding of the nature, the underlying computations, and the neural implementation of motor learning and adaptation in a way that had been possible neither in Drosophila nor in other animal models yet.
Overall, this project proposal allow a better understanding of the nature, the underlying computations, and the neural implementation of motor learning and adaptation in a way that had been possible neither in Drosophila nor in other animal models yet.