Despite extensive theoretical work on biologically plausible learning rules,
clear evidence about whether and how such rules are implemented in the brain
has been difficult to obtain. We consider biologically plausible supervised-
and reinforcement-learning rules and ask whether changes in network activity
during learning can be used to determine which learning rule is being used.
Supervised learning requires a credit-assignment model estimating the mapping
from neural activity to behavior, and, in a biological organism, this model
will inevitably be an imperfect approximation of the ideal mapping, leading to
a bias in the direction of the weight updates relative to the true gradient.
Reinforcement learning, on the other hand, requires no credit-assignment model
and tends to make weight updates following the true gradient direction. We
derive a metric to distinguish between learning rules by observing changes in
the network activity during learning, given that the mapping from brain to
behavior is known by the experimenter. Because brain-machine interface (BMI)
experiments allow for precise knowledge of this mapping, we model a
cursor-control BMI task using recurrent neural networks, showing that learning
rules can be distinguished in simulated experiments using only observations
that a neuroscience experimenter would plausibly have access to.

本文研究了生物可行的监督学习和强化学习规则，并提出了一种基于观察神经网络活动变化鉴别学习规则的方法，进而应用于大脑机器接口实验中，以模拟光标控制任务。