Model calibration using a parallel differential evolution algorithm in computational neuroscience: simulation of stretch induced nerve deficit

• URL: http://arxiv.org/abs/2409.12567v1
• Date: Thu, 19 Sep 2024 08:40:32 GMT
• Title: Model calibration using a parallel differential evolution algorithm in computational neuroscience: simulation of stretch induced nerve deficit
• Authors: Antonio LaTorre, Man Ting Kwong, Julián A. García-Grajales, Riyi Shi, Antoine Jérusalem, José-María Peña,
• Abstract summary: We use a coupled mechanical electrophysiological model with several free parameters that are required to be calibrated against experimental results. The calibration is carried out by means of an evolutionary algorithm (differential evolution, DE) that needs to evaluate each configuration of parameters on six different damage cases. We have developed a parallel implementation based on OpenMP that runs on a multi-processor taking advantage of all the available computational power.
• Score: 1.1026741683718058
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Neuronal damage, in the form of both brain and spinal cord injuries, is one of the major causes of disability and death in young adults worldwide. One way to assess the direct damage occurring after a mechanical insult is the simulation of the neuronal cells functional deficits following the mechanical event. In this study, we use a coupled mechanical electrophysiological model with several free parameters that are required to be calibrated against experimental results. The calibration is carried out by means of an evolutionary algorithm (differential evolution, DE) that needs to evaluate each configuration of parameters on six different damage cases, each of them taking several minutes to compute. To minimise the simulation time of the parameter tuning for the DE, the stretch of one unique fixed-diameter axon with a simplified triggering process is used to speed up the calculations. The model is then leveraged for the parameter optimization of the more realistic bundle of independent axons, an impractical configuration to run on a single processor computer. To this end, we have developed a parallel implementation based on OpenMP that runs on a multi-processor taking advantage of all the available computational power. The parallel DE algorithm obtains good results, outperforming the best effort achieved by published manual calibration, in a fraction of the time. While not being able to fully capture the experimental results, the resulting nerve model provides a complex averaging framework for nerve damage simulation able to simulate gradual axonal functional alteration in a bundle.

Related papers

• Thermodynamics-informed graph neural networks for real-time simulation of digital human twins [2.6811507121199325]
  This paper presents a novel methodology aimed at advancing current lines of research in soft tissue simulation. The proposed approach integrates the geometric bias of graph neural networks with the physical bias derived from the imposition of a metriplectic structure. Based on the adopted methodologies, we propose a model that predicts human liver responses to traction and compression loads in as little as 7.3 milliseconds.
  arXiv  Detail & Related papers  (2024-12-16T18:01:40Z)
• Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning [0.0]
  Biophysical model fitting plays a key role in obtaining quantitative parameters from physiological signals and images. Here, we present a generic computational approach for solving the parameter extraction inverse problem posed by ordinary differential equation (ODE) modeling. This is achieved by formulating a numerical ODE solver to function as a step-wise analytical one, thereby making it compatible with automatic differentiation-based optimization.
  arXiv  Detail & Related papers  (2024-11-10T12:40:33Z)
• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
  ARCANA is a spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits. We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software.
  arXiv  Detail & Related papers  (2024-09-23T11:16:46Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons [0.0]
  We present an algorithmically reinterpreted the adaptive integrate-and-fire (ALIF) model. We obtain over a $50times$ training speedup using small DTs on synthetic benchmarks. We also showcase how our model makes it possible to quickly and accurately fit real electrophysiological recordings of cortical neurons.
  arXiv  Detail & Related papers  (2023-11-19T18:21:45Z)
• Gradual Optimization Learning for Conformational Energy Minimization [69.36925478047682]
  Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks significantly reduces the required additional data. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules.
  arXiv  Detail & Related papers  (2023-11-05T11:48:08Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• Large-scale Neural Solvers for Partial Differential Equations [48.7576911714538]
  Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs. Recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. We examine the applicability of continuous, mesh-free neural solvers for partial differential equations, physics-informed neural networks (PINNs) We discuss the accuracy of GatedPINN with respect to analytical solutions -- as well as state-of-the-art numerical solvers, such as spectral solvers.
  arXiv  Detail & Related papers  (2020-09-08T13:26:51Z)
• Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery [13.657060682152409]
  We investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. We use an open-source da Vinci Surgical System to probe a soft-tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud.
  arXiv  Detail & Related papers  (2020-03-14T00:16:08Z)
• VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model [0.0]
  We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation.
  arXiv  Detail & Related papers  (2020-03-03T09:48:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.