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Controlling the flow and routing of data is a fundamental problem in manydistributed networks, including transportation systems, integrated circuits,and the Internet. In the brain, synaptic plasticity rules have been discoveredthat regulate network activity in response to environmental inputs, whichenable circuits to be stable yet flexible. Here, we develop a newneuro-inspired model for network flow control that only depends on modifyingedge weights in an activity-dependent manner. We show how two fundamentalplasticity rules (long-term potentiation and long-term depression) can be castas a distributed gradient descent algorithm for regulating traffic flow inengineered networks. We then characterize, both via simulation andanalytically, how different forms of edge-weight update rules affect networkrouting efficiency and robustness. We find a close correspondence betweencertain classes of synaptic weight update rules derived experimentally in thebrain and rules commonly used in engineering, suggesting common principles toboth.

Using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks