Université Côte d'Azur, CNRS, INSERM, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne 06560, France.
Université Côte d'Azur, CNRS, LJAD and NeuroMod, Nice 0600, France
eNeuro. 2024 Oct 29;11(10). doi: 10.1523/ENEURO.0436-23.2024. Print 2024 Oct.
The rat dorsomedial (DMS) and dorsolateral striatum (DLS), equivalent to caudate nucleus and putamen in primates, are required for goal-directed and habit behaviour, respectively. However, it is still unclear whether and how this functional dichotomy emerges in the course of learning. In this study, we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous spatial alternation task, from the acquisition to optimized performance. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of simultaneously recorded neurons, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMS and DLS neural networks across learning stages. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite these major differences, the decoding power of both networks increases during learning. These results suggest that DMS and DLS neural networks gradually reorganize in different ways in order to progressively increase their control over the behavioral performance.
大鼠背侧纹状体的背内侧(DMS)和背外侧(DLS)分别对应灵长类动物的尾状核和壳核,分别负责目标导向行为和习惯行为。然而,在学习过程中,这种功能二分法是否以及如何出现仍不清楚。在这项研究中,我们通过记录大鼠在执行连续空间交替任务时 DMS 和 DLS 的单个神经元活动,从获得优化表现的过程中,研究了这个问题。我们首先应用经典的分析方法,根据单个神经元的放电率与特定任务事件或迷宫轨迹的关系,确定与任务相关的活动。然后,我们使用基于 Hawkes 过程的创新方法来重建同时记录的神经元的有向连接图,该方法用于解码动物行为。这种方法使我们能够更好地揭示 DMS 和 DLS 神经网络在学习阶段的作用。我们表明,DMS 和 DLS 在整个学习阶段显示出不同的与任务相关的活动,并且编码神经元的比例随时间的推移在 DMS 中减少,在 DLS 中增加。尽管存在这些主要差异,但两个网络的解码能力在学习过程中都会增加。这些结果表明,DMS 和 DLS 神经网络以不同的方式逐渐重组,以便逐渐增强它们对行为表现的控制。