Cannistraci-Hebb Training of Convolutional Neural Networks

Dynamic sparse training enables neural networks to evolve their topology during training, which reduces computational overhead while maintaining performance. Cannistraci-Hebb Training (CHT), a brain-inspired method based on epitopological learning principles, has demonstrated significant advantages in building ultra-sparse fully connected networks. However, its application to convolutional neural networks (CNNs) faces challenges due to two fundamental constraints inherent in CNNs: receptive field locality and weight-sharing dependency. These constraints prevent the independent link manipulation that is essential for existing CHT frameworks. We propose CHT-Conv, extending CHT to convolutional layers while adhering to the inherent constraints of convolutional layers. Experiments on CIFAR-10 and CIFAR-100 using VGG16 architectures show CHT-Conv achieves competitive or superior performance compared to Sparse Evolutionary Training (SET) at 50% and 70% sparsity levels. This work represents the first successful extension of epitopological learning principles to convolutional architectures, opening new possibilities for brain-inspired sparse training in modern deep learning.