  The human brain can be considered to be a graphical structure comprising of
tens of billions of biological neurons connected by synapses. It has the
remarkable ability to automatically re-route information flow through alternate
paths in case some neurons are damaged. Moreover, the brain is capable of
retaining information and applying it to similar but completely unseen
scenarios. In this paper, we take inspiration from these attributes of the
brain, to develop a computational framework to find the optimal low cost path
between a source node and a destination node in a generalized graph. We show
that our framework is capable of handling unseen graphs at test time. Moreover,
it can find alternate optimal paths, when nodes are arbitrarily added or
removed during inference, while maintaining a fixed prediction time. Code is
available here: https://github.com/hangligit/pathfinding
