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.

翻译：元种群Wilson-Cowan模型作为一种神经群体网络模型，将大脑的不同皮层下区域视为相互连接的节点，节点间的连接代表这些区域之间各种类型的结构、功能或有效神经连接。每个区域包含相互作用的兴奋性与抑制性细胞群，这与标准Wilson-Cowan模型保持一致。通过在此类元种群模型的动力学中引入稳定吸引子，我们将其转化为一种能够实现高精度图像与文本分类的学习算法。我们在MNIST和Fashion MNIST数据集上结合卷积神经网络进行测试，在CIFAR-10和TF-FLOWERS数据集上进行验证，并结合Transformer架构（BERT）在IMDB数据集上评估，均展现出较高的分类准确率。这些数值评估表明，对元种群Wilson-Cowan模型进行最小程度的修改即可揭示独特且先前未被观测到的动力学特性。