Cortical prediction markets
This work provides a novel theoretical framework for interpreting neural computation, which could impact neuroscience and AI, though it is incremental in applying existing concepts to biological systems.
The paper tackles the problem of understanding cortical learning through mechanism design, showing that neurons can be modeled as rational agents maximizing proper scoring rules, and demonstrates that networks using backpropagated incentives can learn simple tasks.
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.