mlsanityData to 14 Oct 2026

Learning in Wilson-Cowan model for metapopulation

arXiv 2406.16453 ↗First posted 24 Jun 2024q-bio.NC cond-mat.dis-nn cond-mat.stat-mechcited by 2

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Abstract (arXiv)

The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions. Each region comprises interacting populations of excitatory and inhibitory cells, consistent with the standard Wilson-Cowan model. By incorporating stable attractors into such a metapopulation model's dynamics, we transform it into a learning algorithm capable of achieving high image and text classification accuracy. We test it on MNIST and Fashion MNIST, in combination with convolutional neural networks, on CIFAR-10 and TF-FLOWERS, and, in combination with a transformer architecture (BERT), on IMDB, always showing high classification accuracy. These numerical evaluations illustrate that minimal modifications to the Wilson-Cowan model for metapopulation can reveal unique and previously unobserved dynamics.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 113 references that are in the map. First posted on arXiv 24 Jun 2024.

Builds on

23 of 113 references.

References · 23

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11 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Approximating velocity fields with planted attractors via Neural-ODEs for classification purposescited by 0
  2. 2026Multiplicity of Stable Attractors in Disordered Neural Modelscited by 0