Learning in Wilson-Cowan model for metapopulation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910727700217856 |
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| author | Marino, Raffaele Buffoni, Lorenzo Chicchi, Lorenzo Di Patti, Francesca Febbe, Diego Giambagli, Lorenzo Fanelli, Duccio |
| author_facet | Marino, Raffaele Buffoni, Lorenzo Chicchi, Lorenzo Di Patti, Francesca Febbe, Diego Giambagli, Lorenzo Fanelli, Duccio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning in Wilson-Cowan model for metapopulation Marino, Raffaele Buffoni, Lorenzo Chicchi, Lorenzo Di Patti, Francesca Febbe, Diego Giambagli, Lorenzo Fanelli, Duccio Neurons and Cognition Disordered Systems and Neural Networks Statistical Mechanics Artificial Intelligence Neural and Evolutionary Computing 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. |
| title | Learning in Wilson-Cowan model for metapopulation |
| topic | Neurons and Cognition Disordered Systems and Neural Networks Statistical Mechanics Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2406.16453 |