# Graph-Based Representation Learning of Neuronal Dynamics and Behavior

arXiv: https://arxiv.org/abs/2410.00665 · HTML: https://mlsanity.com/paper/2410.00665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 22 of 22)

- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://mlsanity.com/paper/1805.11273/) (2018)
- [Learning Structural Node Embeddings Via Diffusion Wavelets](https://mlsanity.com/paper/1710.10321/) (2017)
- [Propagation Kernels](https://mlsanity.com/paper/1410.3314/) (2014)
- [dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning](https://mlsanity.com/paper/1809.02657/) (2018)
- [Variational Graph Recurrent Neural Networks](https://mlsanity.com/paper/1908.09710/) (2019)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Graph Kernels: State-of-the-Art and Future Challenges](https://mlsanity.com/paper/2011.03854/) (2020)
- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Attributed Network Embedding for Learning in a Dynamic Environment](https://mlsanity.com/paper/1706.01860/) (2017)
- [Connectivity Inference from Neural Recording Data: Challenges, Mathematical Bases and Research Directions](https://mlsanity.com/paper/1708.01888/) (2017)
- [The connected brain: Causality, models and intrinsic dynamics](https://mlsanity.com/paper/1602.02945/) (2016)
- [A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification](https://mlsanity.com/paper/2302.07243/) (2023)

## Built on by (most cited first, 2 of 2)

- [A memory-based active inference model of DishBrain-like adaptive behaviour](https://mlsanity.com/paper/2508.06980/) (2025)
- [Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation](https://mlsanity.com/paper/2605.13315/) (2026)

## Abstract (arXiv)

Understanding how neuronal networks reorganize in response to external stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. However, existing methods often fail to capture the evolving structure of neural connectivity in ways that capture its relationship to behavior, especially in dynamic, uncertain, or high-dimensional settings with sufficient resolution or interpretability. We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework that models time-varying neuronal connectivity by integrating probabilistic graph learning with temporal attention mechanisms. TAVRNN learns latent dynamics at the single-unit level while maintaining interpretable population-level representations, to identify key connectivity patterns linked to behavior. TAVRNN generalizes across diverse neural systems and modalities, demonstrating state-of-the-art classification and clustering performance. We validate TAVRNN on three diverse datasets: (1) electrophysiological data from a freely behaving rat, (2) primate somatosensory cortex recordings during a reaching task, and (3) biological neurons in the DishBrain platform interacting with a virtual game environment. Our method outperforms state-of-the-art dynamic embedding techniques, revealing previously unreported relationships between adaptive behavior and the evolving topological organization of neural networks. These findings demonstrate that TAVRNN offers a powerful and generalizable approach for modeling neural dynamics across experimental and synthetic biological systems. Its architecture is modality-agnostic and scalable, making it applicable across a wide range of neural recording platforms and behavioral paradigms.