Synaptic metaplasticity for image processing enhancement in convolutional neural networks

Synaptic metaplasticity is a biological phenomenon shortly defined as the plasticity of synaptic plasticity, meaning that the previous history of the synaptic activity determines its current plasticity. This phe-nomenon interferes with some of the underlying mechanisms that are considered important in memory and learning processes, such as long-term potentiation and long-term depression. In this work, we pro-vide an approach to include metaplasticity in convolutional neural networks to enhance learning in image classification problems. This approach consists of including metaplasticity as a weight update function in the backpropagation stage of convolutional layers. To validate this proposal, we have been used eight different award-winning convolutional neural networks architectures: LeNet-5, AlexNet, GoogLeNet, VGG16, VGG32, ResNet50, DenseNet121 and DenseNet169; trained with four different pop-ular datasets for benchmarking: MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100. Experimental results show that there is a performance enhancement for each of the convolution neural network architectures in all the datasets used. (c) 2021 Published by Elsevier B.V.