  We investigate cortical learning from the perspective of mechanism design.
First, we show that discretizing standard models of neurons and synaptic
plasticity leads to rational agents maximizing simple scoring rules. Second,
our main result is that the scoring rules are proper, implying that neurons
faithfully encode expected utilities in their synaptic weights and encode
high-scoring outcomes in their spikes. Third, with this foundation in hand, we
propose a biologically plausible mechanism whereby neurons backpropagate
incentives which allows them to optimize their usefulness to the rest of
cortex. Finally, experiments show that networks that backpropagate incentives
can learn simple tasks.
