Biology subjects

Ramalingasetty, S. T.

Publications and source records attributed to Ramalingasetty, S. T..

3 recordsLinked to original sources

bioRxiv2025

Mechanisms of adaptive interlimb coordination to sudden ground loss: a neuromusculoskeletal modeling study

Mammals exhibit robust walking across diverse environments, a capability largely attributed to central pattern generators (CPGs) in the spinal cord. Afferent feedback modulates CPG output and plays a critical role in adaptive locomotion, yet its specific contributions remain poorly understood. To investigate this, we used a neuromusculoskeletal model to simulate hindlimb locomotion in spinalized cats encountering a hole and experiencing a sudden loss of ground support, as described in prior experimental studies. The model couples a trunk-andhindlimb musculoskeletal system to a pair of two-level, half-center CPGs--one for each hindlimb. The model reproduced the observed adaptive interlimb coordination that allows cats to maintain walking after the sudden loss of ground support. Notably, the adaptive response emerged without re-optimizing parameters, which were tuned for steady walking in an environment without holes. Nullcline analysis based on dynamical systems theory revealed that afferent feedback mechanisms controlling the transitions between fast and slow neuronal dynamics facilitated adaptive interlimb coordination. These findings provide mechanistic insight into how spinal feedback circuits support robust locomotion through dynamic interactions between the nervous system, the musculoskeletal system, and the environment.

neuroscience↗

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↗

bioRxiv2020

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop betweenexperimental and virtual embodiedneuroscience

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

neuroscience↗