Cortical prediction markets

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. Keywords: incentives for cooperation, multiagent learning, biologically-inspired approaches, prediction markets 1. Introduction How does the brain encode information about the environment into its structure [26]? Inspired by recent work in prediction markets, this paper investigates cortical learning and the neural code from the perspective of mechanism design [15, 18, 2, 3, 1]. To the best of our knowledge it is the first paper to do so.