小柯机器人

美国马里兰州巴尔的摩国家药物滥用研究所Geoffrey Schoenbaum和Yuji K. Takahashi共同合作，近期取得重要工作进展。他们研究提出腹侧被盖区的多巴胺能预测误差反映出多线程预测模型。相关研究成果2023年4月20日在线发表于《自然—神经科学》杂志上。

据介绍，多巴胺神经元的活动与时差强化学习模型中的预测误差有关。这些模型做出了重大的简化假设，特别是关于多巴胺神经元的预测结构，多巴胺神经元由单链时间点状态组成。尽管这种预测结构可以解释在许多研究中观察到的错误信号，但它无法应对受试者可能推断出多个独立事件和结果的情况。

研究人员记录了在这种情况下腹侧被盖区的多巴胺神经元，以测试单流假设的有效性。大鼠接受了基于气味的选择任务的训练，在该任务中，每个试验中提供的几种奖励之一的时间和身份在受试组之间发生了变化。

总之，这一设计揭示了一种错误信号模式，需要多巴胺神经元访问和更新多个独立的预测流，这些预测流反映了受试者对预期奖励的时间和潜在独特身份的信念。

附：英文原文

Title: Dopaminergic prediction errors in the ventral tegmental area reflect a multithreaded predictive model

Author: Takahashi, Yuji K., Stalnaker, Thomas A., Mueller, Lauren E., Harootonian, Sevan K., Langdon, Angela J., Schoenbaum, Geoffrey

Issue&Volume: 2023-04-20

Abstract: Dopamine neuron activity is tied to the prediction error in temporal difference reinforcement learning models. These models make significant simplifying assumptions, particularly with regard to the structure of the predictions fed into the dopamine neurons, which consist of a single chain of timepoint states. Although this predictive structure can explain error signals observed in many studies, it cannot cope with settings where subjects might infer multiple independent events and outcomes. In the present study, we recorded dopamine neurons in the ventral tegmental area in such a setting to test the validity of the single-stream assumption. Rats were trained in an odor-based choice task, in which the timing and identity of one of several rewards delivered in each trial changed across trial blocks. This design revealed an error signaling pattern that requires the dopamine neurons to access and update multiple independent predictive streams reflecting the subject’s belief about timing and potentially unique identities of expected rewards.

DOI: 10.1038/s41593-023-01310-x

Source: https://www.nature.com/articles/s41593-023-01310-x

Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex