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.

从机制设计的角度探究皮层学习的过程，研究表明基于离散化的标准神经元模型和突触可塑性设计下，神经元会根据打分规则达到最优表现，而这些打分规则恰当而准确地表达了神经元突触权重和 Spike 单位的期望效用和高得分结果，基于这些发现，提出了一种生物学上可行的机制，通过反向传播动机来优化神经元对大脑其他部分的作用，并展现了该机制能够实现简单任务的学习。

大脑皮层预测市场

Cortical prediction markets

Despite its size and complexity, the human cortex exhibits striking
anatomical regularities, suggesting there may simple meta-algorithms underlying
cortical learning and computation. We expect such meta-algorithms to be of
interest since they need to operate quickly, scalably and effectively with
little-to-no specialized assumptions.
This note focuses on a specific question: How can neurons use vast quantities
of unlabeled data to speed up learning from the comparatively rare labels
provided by reward systems? As a partial answer, we propose randomized
co-training as a biologically plausible meta-algorithm satisfying the above
requirements. As evidence, we describe a biologically-inspired algorithm,
Correlated Nystrom Views (XNV) that achieves state-of-the-art performance in
semi-supervised learning, and sketch work in progress on a neuronal
implementation.

该论文旨在研究人类大脑皮层学习和计算的简单元算法，提出了基于随机协同训练的生物可行元算法，探索了对训练样本标签稀疏的学习过程，以及提出的算法在半监督学习中的性能表现。