Before revision:  Here, we show that in any physically constructible geometric network, the geometry of the network  constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
After revision:  Here, we address this question by developing a generalized theoretical framework that formally describes how in any physically constructible geometric network, the geometry of the network constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.