NeuRL：用于神经解码的封闭形式逆强化学习
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
摘要：
当前的神经解码方法通常旨在通过监督学习来解释基于神经活动的行为。然而，由于通常受试者的学习与他们对长期奖励的期望之间存在很强的联系，我们提出了NeuRL，一种逆强化学习方法，（1）从封闭形式的受试者的收集轨迹中提取内在奖励函数，（2）将神经信号映射到这种内在奖励，以解释行为中的长期依赖性，以及（3）通过提取Q值和基于这些看不见的神经信号的内在奖励值的相应玻尔兹曼策略来预测这些看不见的神经信号的模拟行为。我们表明，NeuRL导致更好的泛化和改进的解码性能相比，监督的方法。我们研究了大鼠在反应准备任务中的行为，并评估了NeuRL在模拟抑制和试验前行为预测中的表现。通过分配明确的功能角色，定义的神经元群体，我们的方法提供了一个新的解释工具，复杂的神经元数据与可测试的预测。在试验前行为预测中，与传统方法相比，我们的方法还将准确率提高了15%。
英文摘要：
Current neural decoding methods typically aim at explaining behavior based on neural activity via supervised learning. However, since generally there is a strong connection between learning of subjects and their expectations on long-term rewards, we propose NeuRL, an inverse reinforcement learning approach that (1) extracts an intrinsic reward function from collected trajectories of a subject in closed form, (2) maps neural signals to this intrinsic reward to account for long-term dependencies in the behavior and (3) predicts the simulated behavior for unseen neural signals by extracting Q-values and the corresponding Boltzmann policy based on the intrinsic reward values for these unseen neural signals. We show that NeuRL leads to better generalization and improved decoding performance compared to supervised approaches. We study the behavior of rats in a response-preparation task and evaluate the performance of NeuRL within simulated inhibition and per-trial behavior prediction. By assigning clear functional roles to defined neuronal populations our approach offers a new interpretation tool for complex neuronal data with testable predictions. In per-trial behavior prediction, our approach furthermore improves accuracy by up to 15% compared to traditional methods.
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