Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Neuron. 2022 Nov 16;110(22):3789-3804.e9. doi: 10.1016/j.neuron.2022.08.022. Epub 2022 Sep 20.
Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to characterize the behavior of mice freely interacting with a novel object. Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of the striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability are explained by a reinforcement-learning (RL) model of threat prediction in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus") and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.
动物在环境中既会探索新物体，也会回避新物体，但这些行为及其动态的神经机制仍未被描述。在这里，我们使用多点跟踪（DeepLabCut）和行为分割（MoSeq）来描述小鼠与新物体自由互动时的行为。新颖性引发了一系列特征性的行为，首先是探究性接近，最终是物体的接触或回避。纹状体尾部（TS）的多巴胺抑制接触，并且多巴胺反应可预测个体行为的可变性。行为动态和个体可变性可以用威胁预测的强化学习（RL）模型来解释，其中行为源于新颖性引起的初始威胁预测（类似于“塑造奖励”）和通过多巴胺介导的威胁预测误差来学习的威胁预测。这些结果揭示了与奖励和威胁相关的多巴胺子系统之间的算法相似性。
