皮质 - 基底 - 甲状腺子网络如何改变决策政策以最大限度地提高奖励率
Jyotika Bahuguna1, Timothy Verstynen1,2, Jonathan E Rubin2,3
1Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
bioRxiv : the preprint server for biology
|June 3, 2024
概括
哺乳动物使用皮质 - 基底腺体 - 甲状腺体通路来进行灵活的决策. 这些电路中的多巴胺驱动的可塑性优化了提高奖励率的决策.
科学领域:
- 计算神经科学是一种计算神经科学.
- 系统神经科学 系统神经科学
- 决策神经科学 神经科学
背景情况:
- 哺乳动物的决策政策依赖于皮质 - 基底 - 脑垂体 (CBGT) 途径.
- 了解CBGT电路内决策政策的经验依赖转变是一个挑战.
研究的目的:
- 通过计算建模CBGT电路如何产生灵活的决策策略.
- 阐明由多巴胺塑性驱动的与学习相关的政策转变的机制.
主要方法:
- 在无奖励条件下使用尖端神经网络模拟CBGT电路.
- 将证据积累模型与模拟行为相匹配.
- 使用正统的相关性分析来识别对照组 (响应性,弹性,选择性).
- 在两个选择任务中模拟学习,有奖励反和多巴胺活性可塑性.
主要成果:
- 确定了三个CBGT控制集,对证据积累参数进行映射.
- 证明在皮层-状突触上的多巴胺活性可塑性可以控制速度-精度的权衡.
- 通过调节证据利用,学习增加了奖励率.
结论:
- CBGT 电路表现出不同的控制集体,影响决策过程.
- 多巴胺活性可塑性根据奖励预测错误动态调整这些合奏.
- 这为优化决策策略提供了一种机制,以最大限度地提高随时间推移的奖励率.
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