.png){kind=icon}
返回
Hippocampal pattern separation supports reinforcement learning
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-019-08998-1.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Animals rely on learned associations to make decisions. Associations can be based on relationships between object features (e.g., the three leaflets of poison ivy leaves) and outcomes (e.g., rash). More often, outcomes are linked to multidimensional states (e.g., poison ivy is green in summer but red in spring). Feature-based reinforcement learning fails when the values of individual features depend on the other features present. One solution is to assign value to multi-featural conjunctive representations. Here, we test if the hippocampus forms separable conjunctive representations that enables the learning of response contingencies for stimuli of the form: AB+, B-, AC-, C+. Pattern analyses on functional MRI data show the hippocampus forms conjunctive representations that are dissociable from feature components and that these representations, along with those of cortex, influence striatal prediction errors. Our results establish a novel role for hippocampal pattern separation and conjunctive representation in reinforcement learning.
Keyword:
ORBITOFRONTAL CORTEX
PREFRONTAL CORTEX
NUCLEUS-ACCUMBENS
MEMORY
REPRESENTATION
PREDICTION
SYSTEMS
ASSOCIATION
MECHANISMS
STRIATUM
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
Preference by Association: How Memory Mechanisms in the Hippocampus Bias Decisions.png)
SCIENCE
SCIENCEIF45.8
PREAI
分享收藏The hippocampal-striatal axis in learning, prediction and goal-directed behavior学习，预测和目标导向行为中的海马-纹状体轴TRENDS IN NEUROSCIENCES
TRENDS IN NEUROSCIENCESIF15.1
PREAI
分享收藏WHY THERE ARE COMPLEMENTARY LEARNING-SYSTEMS IN THE HIPPOCAMPUS AND NEOCORTEX - INSIGHTS FROM THE SUCCESSES AND FAILURES OF CONNECTIONIST MODELS OF LEARNING AND MEMORY为什么在海马和新大脑皮层中存在互补的学习系统-来自学习和记忆的连接主义模型的成功和失败的见解PSYCHOLOGICAL REVIEW
PSYCHOLOGICAL REVIEWIF5.8
PREAI
分享收藏
查看更多内容