Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Iyengar, Raghu Sesha; Pithapuram, Madhav Vinodh; Singh, Amreek; Raghavan, Madhusudan

doi:10.3389/fninf.2019.00056

Cited by 10 publications

(14 citation statements)

References 179 publications

(189 reference statements)

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“…Interfaces to the cortical structures and Rhythm Generation are also implemented to modulate the activity of the Pattern Formation network. (b) Figure shows the cell groups and synaptic connections between them that are modelled in NEUROiD ([36]). We modeled the C5 and C6 segments of the spinal cord.…”

Section: Methodsmentioning

confidence: 99%

“…While significant work has been done in the area of developing such motor components, development of infrastructure for integrated and curated movement infrastructure has been a recent development. Co-simulation platform NEUROiD ( [36]) provides a platform that allows integration of state-of-the-art components in musculoskeletal and neuronal domains at various scales in a modular fashion. Building on this work, composite neuro-musculoskeletal upper limb systems have been developed ( [78]).…”

Section: An Integrated Stack Of Motor Simulation Components Generating Virtual Physiological Movementmentioning

confidence: 99%

“…In our previous work ( [36]), we introduced the NEUROiD platform which supports building large multiscale and multidiscipline neuromotor models. NEUROiD allows micro, meso and macro level definitions of the anatomical, physiological and biomechanical components of the model.…”

Section: Building a Biologically Realistic Model Of Upper Limb Along With Controller Circuitry In Spinal Cordmentioning

confidence: 99%

“…A 3D rendering of the model with the laminae boundaries, spinal segment outline, cell instances and network connections can be seen in figure 1. We refer the reader to [36] and [78] for further details on the NEUROiD platform and the upper limb model respectively.…”

Section: Building a Biologically Realistic Model Of Upper Limb Along With Controller Circuitry In Spinal Cordmentioning

confidence: 99%

“…In this work we demonstrate for the first time a class of simple movements using such an exemplar system in the form of a modular, neuro-musculoskeletal upper limb. Built on the NEUROiD platform ([36]) as a co-simulation of the popular open neuronal simulator NEURON ([37]) and musculoskeletal simulator OpenSim ([38]), it allows a choice of model components from both domains in a modular fashion. Such virtual digital models exhibiting movement can play extremely important roles in scientific investigations into movement disorders ([25,28]), development of therapies ([39,40]) and rehabilitation tools ([41,42]) for the same.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Reinforcement Learning based approach for movement training of neuro-musculoskeletal systems

Kapardi

Raghavan

2021

Preprint

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Mechanisms behind neural control of movement have been an active area of research. Goal-directed movement is a common experimental setup used to understand these mechanisms and neural pathways. On the one hand, optimal feedback control theory is used to model and make quantitative predictions of the coordinated activations of the effectors, such as muscles, joints or limbs. While on the other hand, evidence shows that higher centres such as Basal Ganglia and Cerebellum are involved in activities such as reinforcement learning and error correction. In this paper, we provide a framework to build a digital twin of relevant sections of the human spinal cord using our NEUROiD platform. The digital twin is anatomically and physiologically realistic model of the spinal cord at cellular, spinal networks and system level. We then build a framework to learn the supraspinal activations necessary to perform a simple goal directed movement of the upper limb. The NEUROiD model is interfaced to an Opensim model for all the musculoskeletal simulations. We use Deep Reinforcement Learning to obtain the supraspinal activations necessary to perform the goal directed movement. As per our knowledge, this is the first time an attempt is made to learn the stimulation pattern at the spinal cord level, especially by limiting the observation space to only the afferent feedback received on the Ia, II and Ib fibers. Such a setup results in a biologically realistic constrained environment for learning. Our results show that (1) Reinforcement Learning algorithm converges naturally to the triphasic response observed during goal directed movement (2) Increasing the complexity of the goal gradually was very important to accelerate learning (3) Modulation of the afferent inputs were sufficient to execute tasks which were not explicitly learned, but were closely related to the learnt task.

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Section: Methodsmentioning

confidence: 99%

Section: An Integrated Stack Of Motor Simulation Components Generating Virtual Physiological Movementmentioning

confidence: 99%

Section: Building a Biologically Realistic Model Of Upper Limb Along With Controller Circuitry In Spinal Cordmentioning

confidence: 99%

Section: Building a Biologically Realistic Model Of Upper Limb Along With Controller Circuitry In Spinal Cordmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Reinforcement Learning based approach for movement training of neuro-musculoskeletal systems

Kapardi

Raghavan

2021

Preprint

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Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

Homs‐Pons,

Lautenschlager,

Schmid

et al. 2024

GAMM-Mitteilungen

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The functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.

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The NEUROiD neuromusculoskeletal movement simulation platform

Iyengar¹,

Kapardi²,

Singh³

et al. 2023

Digital Human Modeling and Medicine

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Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Cited by 10 publications

References 179 publications

A Deep Reinforcement Learning based approach for movement training of neuro-musculoskeletal systems

A Deep Reinforcement Learning based approach for movement training of neuro-musculoskeletal systems

Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

The NEUROiD neuromusculoskeletal movement simulation platform

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