Using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks
This work addresses flow control in distributed networks like transportation and the Internet, but it is incremental as it adapts known biological principles to engineering contexts.
The authors tackled the problem of controlling traffic flow in distributed networks by developing a neuro-inspired model based on synaptic plasticity rules, showing that these rules can be cast as a distributed gradient descent algorithm and finding a correspondence between brain-derived rules and engineering principles.
Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that only depends on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules (long-term potentiation and long-term depression) can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both via simulation and analytically, how different forms of edge-weight update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.