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Related Experiment Videos

Dynamic memory-enhanced recurrent neural networks with temporal attention for robust long-range connectivity

June Sic Kim1

  • 1Clinical Research Institute, Konkuk University Medical Center, 120-1 Neungdong-ro Gwangjin-gu, Seoul, 05030, South Korea.

Neuroimage
|June 19, 2026
PubMed
Summary

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Identification of Cortices with Characteristics of Rhythmic Entrainment and Its Periodicity.

Experimental neurobiology·2025

We developed a dynamic memory-enhanced LSTM with temporal attention (DM+Attention) to improve long-range directed connectivity estimation in neural networks. This method significantly enhances gradient-based connectivity inference accuracy, outperforming existing models.

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Accurate estimation of long-range directed connectivity is crucial for understanding neural networks but is challenging due to vanishing gradients.
  • Conventional recurrent neural networks struggle with modeling long-range dependencies and preserving temporal information.

Purpose of the Study:

  • To propose a novel dynamic memory-enhanced LSTM architecture with a temporal attention mechanism (DM+Attention) for improved gradient-based connectivity inference.
  • To enhance the modeling of long-range dependencies and the accuracy of connectivity estimation in time-series data.

Main Methods:

  • Introduced a trainable dynamic memory matrix to improve long-range dependency modeling.
  • Integrated a temporal attention mechanism to refine gradient estimates by focusing on relevant time steps.
Keywords:
Brain connectivityDynamic memoryIntegrated gradientsNoise distribution-based statisticsTemporal attention

Related Experiment Videos

  • Compared DM+Attention with bidirectional LSTM, Transformer encoder, and multilayer perceptron using synthetic time-series data.

    • Main Results:

    • DM+Attention achieved a mean AUC of 0.959, significantly outperforming bLSTM (25.7%), Transformer encoder (16.0%), and partial directed coherence (19.2%).
    • Performance gains were more pronounced under dense connectivity conditions, with DM+Attention achieving a mean AUC of 0.907.
    • Achieved high correlation coefficients (R²=0.986 sparse, R²=0.691 dense) in continuous connectivity-weight prediction.

    Conclusions:

    • The combination of dynamic memory and temporal attention effectively captures complex temporal dependencies for enhanced connectivity inference.
    • The proposed framework is scalable, biologically plausible, and applicable to neuroimaging data (EEG, MEG, fMRI).
    • Introduced an efficient non-parametric statistical approach for distinguishing genuine connectivity from noise.