Latent Graph Learning in Generative Models of Neural Signals

• URL: http://arxiv.org/abs/2508.16776v1
• Date: Fri, 22 Aug 2025 20:19:56 GMT
• Title: Latent Graph Learning in Generative Models of Neural Signals
• Abstract summary: Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience.<n>Here we explore latent graph learning in generative models of neural signals.
• Score: 0.6774024053289015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience. Foundation models for large-scale neural data represent shared latent structures of neural signals. However, extracting interpretable latent graph representations in foundation models remains challenging and unsolved. Here we explore latent graph learning in generative models of neural signals. By testing against numerical simulations of neural circuits with known ground-truth connectivity, we evaluate several hypotheses for explaining learned model weights. We discover modest alignment between extracted network representations and the underlying directed graphs and strong alignment in the co-input graph representations. These findings motivate paths towards incorporating graph-based geometric constraints in the construction of large-scale foundation models for neural data.

Related papers

• Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks [0.0]
  Inferring synaptic connectivity from neural population activity is a fundamental challenge in computational neuroscience.<n>We propose a graph-based neural inference model that simultaneously predicts neural activity and infers latent connectivity by modeling neurons as interacting nodes in a graph.
  arXiv  Detail & Related papers  (2025-09-21T17:46:43Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting [60.03169701753824]
  We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting. The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
  arXiv  Detail & Related papers  (2023-05-16T11:38:19Z)
• GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network [7.876789380671075]
  We propose a graph neural network approach with score-based method aiming at learning a sparse DAG. We demonstrate methods with graph neural network significantly outperformed other state-of-the-art methods with dynamic bayesian networking inference.
  arXiv  Detail & Related papers  (2023-01-28T02:49:13Z)
• Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model [64.29487107585665]
  Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
  arXiv  Detail & Related papers  (2022-07-14T20:03:52Z)
• Relational representation learning with spike trains [0.0]
  We present a model that allows us to learn spike train-based embeddings of knowledge graphs, requiring only one neuron per graph element by fully utilizing the temporal domain of spike patterns. In general, the presented results show how relational knowledge can be integrated into spike-based systems, opening up the possibility of merging event-based computing and data to build powerful and energy efficient artificial intelligence applications and reasoning systems.
  arXiv  Detail & Related papers  (2022-05-18T18:00:37Z)
• Hyperbolic Graph Learning: A Comprehensive Review [56.53820115624101]
  This survey paper provides a comprehensive review of the rapidly evolving field of Hyperbolic Graph Learning (HGL)<n>We systematically categorize and analyze existing methods dividing them into (1) hyperbolic graph embedding-based techniques, (2) graph neural network-based hyperbolic models, and (3) emerging paradigms.<n>We extensively discuss diverse applications of HGL across multiple domains, including recommender systems, knowledge graphs, bioinformatics, and other relevant scenarios.
  arXiv  Detail & Related papers  (2022-02-28T15:08:48Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• deepstruct -- linking deep learning and graph theory [1.4504054468850667]
  deepstruct connects deep learning models and graph theory. Deepstruct provides deep neural network models with different restrictions which can be created based on an initial graph.
  arXiv  Detail & Related papers  (2021-11-12T11:58:13Z)
• Sampling and Recovery of Graph Signals based on Graph Neural Networks [44.76396026242879]
  We propose interpretable graph neural networks for sampling and recovery of graph signals, respectively. The proposed methods are able to flexibly learn a variety of graph signal models from data by leveraging the learning ability of neural networks. In the experiments, we illustrate the effects of the proposed graph neural sampling and recovery modules and find that the modules can flexibly adapt to various graph structures and graph signals.
  arXiv  Detail & Related papers  (2020-11-03T01:45:41Z)
• Generalizable Machine Learning in Neuroscience using Graph Neural Networks [0.0]
  We show that neural networks perform remarkably well on both neuron-level dynamics prediction, and behavioral state classification. In our experiments, we found that graph neural networks generally outperformed structure models and excel in generalization on unseen organisms.
  arXiv  Detail & Related papers  (2020-10-16T18:09:46Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Graph Structure of Neural Networks [104.33754950606298]
  We show how the graph structure of neural networks affect their predictive performance. A "sweet spot" of relational graphs leads to neural networks with significantly improved predictive performance. Top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks.
  arXiv  Detail & Related papers  (2020-07-13T17:59:31Z)

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.