  The cohesiveness of response to external stimuli depends on rapid
distortion-free information transfer across the network. Aligning with the
information from the network has been used to model such information transfer.
Nevertheless, the rate of such diffusion-type, neighbor-based information
transfer is limited by the update rate at which each individual can sense and
process information. Moreover, models of the diffusion-type information
transfer do not predict the superfluid-like information transfer observed in
nature. The contribution of this article is to show that self reinforcement,
where each individual augments its neighbor-averaged information update using
its previous update, can (i) increase the information-transfer rate without
requiring an increased, individual update-rate; and (ii) capture the observed
superfluid-like information transfer. This improvement in the
information-transfer rate without modification of the network structure or
increase of the bandwidth of each agent can lead to better understanding and
design of networks with fast response.
