Topic: optimal decision boundary

Numerous biologically inspired models for neurons have been developed over the years, from the Hodgkin-Huxley equations to the McCulloch-Pitts model, spanning varying degrees of computational complexity. In particular, Rosenblatt's perceptron model has endured as a standard neuron model used in machine learning. In this work, we show how perceptron models of varying complexity can be derived by viewing a biological neuron as a bang-off optimal controller. We consider a single neuron as a control agent with binary states---namely, firing and quiescent states---embedded in a closed-loop system