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<p>Understanding how neuronal networks reorganize in response to stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. Existing methods, however, often struggle to capture the evolving structure of neural connectivity and its relationship to behavior, particularly in dynamic, uncertain, or high-dimensional settings with sufficient resolution and interpretability. We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a framework for modeling time-varying neuronal connectivity by integrating probabilistic graph learning with temporal attention. TAVRNN learns single-unit latent dynamics while maintaining interpretable population-level representations, enabling identification of behaviorally relevant connectivity patterns. It generalizes across neural systems differing in scale, behavioral structure, and supervision regime, achieving state-of-the-art classification and clustering performance. We validate TAVRNN on three datasets: (1) electrophysiological recordings from a freely behaving rat, (2) primate somatosensory cortex recordings during reaching task, and (3) biological neurons in the <i>DishBrain</i>platform interacting with a virtual game environment. TAVRNN outperforms state-of-the-art dynamic embedding techniques and reveals previously unreported relationships between adaptive behavior and evolving network topology. These findings establish TAVRNN as a powerful, scalable, generalizable, and modality-agnostic approach for modeling neural dynamics across experimental and synthetic biological systems, with applicability across neural recording platforms and behavioral paradigms.</p>

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Graph-based representation learning of neuronal dynamics and behavior

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

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  • Moein Khajehnejad,
  • Forough Habibollahi,
  • Ahmad Khajehnejad,
  • Chris French,
  • Brett J. Kagan,
  • Adeel Razi

摘要

Understanding how neuronal networks reorganize in response to stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. Existing methods, however, often struggle to capture the evolving structure of neural connectivity and its relationship to behavior, particularly in dynamic, uncertain, or high-dimensional settings with sufficient resolution and interpretability. We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a framework for modeling time-varying neuronal connectivity by integrating probabilistic graph learning with temporal attention. TAVRNN learns single-unit latent dynamics while maintaining interpretable population-level representations, enabling identification of behaviorally relevant connectivity patterns. It generalizes across neural systems differing in scale, behavioral structure, and supervision regime, achieving state-of-the-art classification and clustering performance. We validate TAVRNN on three datasets: (1) electrophysiological recordings from a freely behaving rat, (2) primate somatosensory cortex recordings during reaching task, and (3) biological neurons in the DishBrainplatform interacting with a virtual game environment. TAVRNN outperforms state-of-the-art dynamic embedding techniques and reveals previously unreported relationships between adaptive behavior and evolving network topology. These findings establish TAVRNN as a powerful, scalable, generalizable, and modality-agnostic approach for modeling neural dynamics across experimental and synthetic biological systems, with applicability across neural recording platforms and behavioral paradigms.