Distinguishing Learning Rules with Brain Machine Interfaces
Distinguishing Learning Rules with Brain Machine Interfaces
复制标题
用脑机接口区分学习规则
DOI：
10.48550/arxiv.2206.13448
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
James M. Murray
中科院分区：
文献类型：
--
作者：
Jacob P. Portes;Christian Schmid;James M. Murray
尽管对生物学上合理的学习规则进行了大量的理论工作，但很难获得关于这些规则是否以及如何在大脑中实施的明确证据。我们考虑生物学上合理的监督学习和强化学习规则，并询问学习过程中网络活动的变化是否可以用来确定正在使用哪种学习规则。监督学习需要一个信用分配模型来估计从神经活动到行为的映射，并且在生物有机体中，该模型将不可避免地是理想映射的不完美近似，导致权重更新方向相对于真实梯度的偏差。另一方面，强化学习不需要信用分配模型，并且倾向于按照真实梯度方向进行权重更新。假设实验者已知从大脑到行为的映射，我们通过观察学习过程中网络活动的变化来得出区分学习规则的度量。由于脑机接口 (BMI) 实验可以精确了解这种映射，因此我们使用循环神经网络对光标控制 BMI 任务进行建模，表明仅使用神经科学实验者可能能够访问的观察结果即可在模拟实验中区分学习规则。
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.
影响因子：
13.9
作者：
Peters AJ;Liu H;Komiyama T
通讯作者：
Komiyama T
DOI：
--
发表时间：
2019-07
期刊：
ArXiv
影响因子：
--
作者：
O. Marschall;Kyunghyun Cho;Cristina Savin
通讯作者：
O. Marschall;Kyunghyun Cho;Cristina Savin
DOI：
--
发表时间：
2022
期刊：
International Conference on Learning Representations
影响因子：
--
作者：
Kepple, D.;Engelken, R.;Rajan, K
通讯作者：
Rajan, K