Is Vascular Architecture Tailored to Bring Efficient Neural Performance? A Study of Artificial Neurovascular Networks (ANVN)

Abstract

Neural networks perform classification and functional-approximation tasks with reasonably high accuracy when trained optimally. However, unlike the neurons in artificial neural networks, biological neurons require energy to function. This energy is supplied by an extensive network of blood vessels. In this study we explore the effect of a trained vascular network on the neural network performance. The ANVN comprises a single layered MLP connected bidirectionally to the vascular tree structure. The root node of the vascular tree structure is an energy source and the terminal leaf nodes supply energy to the hidden neurons of the MLP. The branch weights depict the energy split from the parent node to the child node. The leaf node energies determine the bias of the hidden neuron. When analysing the test performance of ANVN for trained and untrained vascular networks, we found that higher performance is achieved for lower root-energies when the vascular network is trained.