  Deep learning is a subset of a broader family of machine learning methods
based on learning data representations. These models are inspired by human
biological nervous systems, even if there are various differences pertaining to
the structural and functional properties of biological brains. The elementary
constituents of deep learning models are neurons, which can be considered as
functions that receive inputs and produce an output that is a weighted sum of
the inputs fed through an activation function. Several models of neurons were
proposed in the course of the years that are all based on learnable parameters
called weights. In this paper we present a new type of artificial neuron, the
double-weight neuron,characterized by additional learnable weights that lead to
a more complex and accurate system. We tested a feed-forward and convolutional
neural network consisting of double-weight neurons on the MNIST dataset, and we
tested a convolution network on the CIFAR-10 dataset. For MNIST we find a
$\approx 4\%$ and $\approx 1\%$ improved classification accuracy, respectively,
when compared to a standard feed-forward and convolutional neural network built
with the same sets of hyperparameters. For CIFAR-10 we find a $\approx 12\%$
improved classification accuracy. We thus conclude that this novel artificial
neuron can be considered as a valuable alternative to common ones.
