Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion

• URL: http://arxiv.org/abs/2405.16503v2
• Date: Mon, 18 Nov 2024 09:28:57 GMT
• Title: Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion
• Authors: Masahito Uwamichi, Simon K. Schnyder, Tetsuya J. Kobayashi, Satoshi Sawai,
• Abstract summary: We present a novel deep learning framework for estimating the underlying equations of motion from observed trajectories. Our framework integrates graph neural networks with neural differential equations, enabling effective prediction of two-body interactions.
• Score: 0.0
• License:
• Abstract: Analyzing the motion of multiple biological agents, be it cells or individual animals, is pivotal for the understanding of complex collective behaviors. With the advent of advanced microscopy, detailed images of complex tissue formations involving multiple cell types have become more accessible in recent years. However, deciphering the underlying rules that govern cell movements is far from trivial. Here, we present a novel deep learning framework for estimating the underlying equations of motion from observed trajectories, a pivotal step in decoding such complex dynamics. Our framework integrates graph neural networks with neural differential equations, enabling effective prediction of two-body interactions based on the states of the interacting entities. We demonstrate the efficacy of our approach through two numerical experiments. First, we used simulated data from a toy model to tune the hyperparameters. Based on the obtained hyperparameters, we then applied this approach to a more complex model with non-reciprocal forces that mimic the collective dynamics of the cells of slime molds. Our results show that the proposed method can accurately estimate the functional forms of two-body interactions -- even when they are nonreciprocal -- thereby precisely replicating both individual and collective behaviors within these systems.

Related papers

• Connectivity structure and dynamics of nonlinear recurrent neural networks [46.62658917638706]
  We develop a theory to analyze how structure in connectivity shapes the high-dimensional, internally generated activity of neural networks. Our theory provides tools to relate neural-network architecture and collective dynamics in artificial and biological systems.
  arXiv  Detail & Related papers  (2024-09-03T15:08:37Z)
• Decomposing heterogeneous dynamical systems with graph neural networks [0.16492989697868887]
  We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneous system. The learned latent structure and dynamics can be used to virtually decompose the complex system.
  arXiv  Detail & Related papers  (2024-07-27T04:03:12Z)
• Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems [70.84976977950075]
  Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. We propose a novel model that captures the continuous interaction among agents using a Graph Neural Network (GNN) as the ODE function. The key innovation of our model is to learn time-dependent representations of treatments and incorporate them into the ODE function, enabling precise predictions of potential outcomes.
  arXiv  Detail & Related papers  (2024-02-29T23:07:07Z)
• 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)
• Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures [0.9821874476902972]
  We focus on the data-driven discovery of a general second-order particle-based model. We present applications to modeling two real-world fish motion datasets.
  arXiv  Detail & Related papers  (2023-11-01T23:45:15Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles [3.625425081454343]
  Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity. We propose feature sharing across neural tuning curves, which significantly improves performance and leads to better-behaved optimization.
  arXiv  Detail & Related papers  (2022-10-06T18:37:49Z)
• Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population [0.0]
  We develop WSINDy for second order IPSs to model the movement of communities of cells. Our approach learns the interaction rules that govern the dynamics of a heterogeneous population of migrating cells. We demonstrate the efficiency and proficiency of the method on several test scenarios, motivated by common cell migration experiments.
  arXiv  Detail & Related papers  (2022-04-29T15:00:21Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

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.