Constructing convolutional neural network by utilizing nematode connectome: A brain-inspired method

Convolutional neural networks have achieved impressive results in areas such as computer vision tasks. Recently, more complex architectures have been designed that add additional operations and connections to the standard architecture to enable deeper network training. A large number of trainable parameters are often required to acquire accurate results. As the number of parameters increases, the network structure becomes more intricate and complex, resulting in low model accuracy and wasted computational resources. Motivated by the promising performance of brain-inspired neural computation principles, a new network structure can be constructed using a composition of the biological connectome. The nematode Caenorhabditis elegans (C. elegans), as the only organism whose connectome has been fully mapped, is suitable to be selected for the construction of neural network structures. In this paper, the neuron connectome of C. elegans is encapsulated as a connectome block and used as the connection pattern of the convolutional layer in the convolutional network. Therefore, this modified convolutional neural network structure is called the nematode connectome neural network (NCNN), and the detailed implementation is presented. Moreover, comparative experiments on the CIFAR-100, CIFAR-10, MNIST, and ImageNet datasets are conducted to fully demonstrate the effectiveness and feasibility of the proposed NCNN model on image classification. In addition, the success of the NCNN also provides new ideas for future network structure design. The source code is publicly available at https://github.com/LongJin-lab/Nematode-Connectome-Neural-Network.

Automated summary

The nematode connectome neural network (NCNN) is a modified convolutional neural network structure that uses the connectome of the nematode Caenorhabditis elegans as the connection pattern. It improves the accuracy and computational efficiency of image classification. Experimental results on multiple datasets demonstrate the effectiveness of the proposed NCNN model.