# Learning in Wilson-Cowan model for metapopulation

arXiv: https://arxiv.org/abs/2406.16453 · HTML: https://mlsanity.com/paper/2406.16453/

## 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 (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?](https://mlsanity.com/paper/1810.09102/) (2018)
- [Attractor and integrator networks in the brain](https://mlsanity.com/paper/2112.03978/) (2021)
- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [No Routing Needed Between Capsules](https://mlsanity.com/paper/2001.09136/) (2020)
- [Fine-Tuning DARTS for Image Classification](https://mlsanity.com/paper/2006.09042/) (2020)
- [Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes](https://mlsanity.com/paper/2012.07747/) (2020)
- [CIFAR-10 Image Classification Using Feature Ensembles](https://mlsanity.com/paper/2002.03846/) (2020)
- [Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)](https://mlsanity.com/paper/2311.10387/) (2023)
- [Complex Recurrent Spectral Network](https://mlsanity.com/paper/2312.07296/) (2023)
- [LlamBERT: Large-scale low-cost data annotation in NLP](https://mlsanity.com/paper/2403.15938/) (2024)
- [Before and beyond the Wilson-Cowan equations](https://mlsanity.com/paper/1907.07821/) (2019)
- [Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity](https://mlsanity.com/paper/2009.02038/) (2020)
- [Beyond Wilson-Cowan dynamics: oscillations and chaos without inhibition](https://mlsanity.com/paper/2204.00583/) (2022)
- [Coarse-graining and criticality in the human connectome](https://mlsanity.com/paper/2310.00139/) (2023)

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

- [Approximating velocity fields with planted attractors via Neural-ODEs for classification purposes](https://mlsanity.com/paper/2606.23550/) (2026)
- [Multiplicity of Stable Attractors in Disordered Neural Models](https://mlsanity.com/paper/2607.22047/) (2026)

## 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.