Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, revealing how dynamics in the corticostriatal system support task learning.

学习根据行为的价值选择适当的行为是适应行为的基础。这种学习形式得到额叶纹状体系统的支持。紧密相连的背侧前额叶皮层（dlPFC）和背侧纹状体（dSTR）是该电路中的关键节点。包括神经生理学记录在内的大量实验证据表明，这些结构中的神经元代表了学习的关键方面。但是，影响神经生理反应的计算机制尚不清楚。为了检查这一点，我们开发了dlPFC-dSTR电路的递归神经网络（RNN）模型，并在动眼神经序列学习任务中对其进行了训练。我们将模型生成的活动与在同一任务中从猴子dlPFC和dSTR记录的活动进行了比较。该网络由编码动作值的纹状体部分和选择适当动作的前额叶部分组成。在训练之后，该系统能够自主地表示和更新动作值并选择动作，从而能够近似逼近皮质口录音中的表示结构。我们发现，学习选择正确的动作会使动作序列表示在模型和神经数据中的活动空间进一步分开。该模型显示，学习是通过增加序列特定表示之间的距离来进行的。这使得模型更有可能随着学习的发展而选择适当的动作序列。因此，我们的模型支持以下假设：网络中的学习将行为的神经表示进一步分开，随着学习的进行，增加了网络产生正确动作的可能性。总而言之，这项研究提高了我们对神经回路动力学如何参与神经计算的理解，揭示了皮质皮质系统中的动力学如何支持任务学习。