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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Graph embeddings for identifying symmetries in Connectomes.

Haozhe Shan1,2, Ashok Litwin-Kumar1

  • 1Kavli Institute for Brain Science, Department of Neuroscience, Columbia University.

Biorxiv : the Preprint Server for Biology
|December 19, 2025
PubMed
Summary
This summary is machine-generated.

We developed a graph embedding algorithm to find symmetries in neural circuits. This method reveals how neural networks process information and which cell types are involved.

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Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Connectomics

Background:

  • Neural circuit models often organize synaptic connections based on processed variables.
  • Canonical models for head direction, spatial navigation, and orientation selectivity exhibit symmetries related to angular and spatial variables.

Purpose of the Study:

  • To develop a graph embedding algorithm for identifying symmetries in neural connectomes.
  • To differentiate between cell-type-specific structure and circuit symmetries.

Main Methods:

  • A novel graph embedding algorithm was developed.
  • The algorithm was applied to the Drosophila brain connectome and a synthetic grid cell network.

Main Results:

  • The method successfully identified rotational and translational symmetries in Drosophila heading direction and visual projection neuron circuits.
  • Toroidal symmetry was identified in a synthetic medial entorhinal cortex grid cell connectome.
  • The embedding geometries revealed latent variables processed by the circuits.

Conclusions:

  • The developed algorithm effectively identifies symmetries in neural circuits.
  • This approach can uncover the latent variables and cell types underlying neural computations.
  • It offers a new tool for analyzing complex neural network architectures.