Neural circuits for learning under perceptual uncertainty
Neural circuits for learning under perceptual uncertainty
批准号:
EP/X026655/1
负责人:
Armin Lak
金额:
$164.54万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
在一个不确定的世界里,大脑是如何学会做出有效决策的?生物学习的一个特点是策略发现:在很长一段时间内,动物可以通过一系列行为从naïve过渡到专家表现。几十年的研究强调了中脑多巴胺(DA)神经元以及主要的多巴胺接收区域纹状体和额叶皮层在学习中的作用。然而,之前的研究并没有从任务的第一次试验中探索这些神经回路,而动物在感知不确定性下测试和选择可能的获得奖励的策略。此外,过去的研究经常在小范围和孤立的情况下检查这些神经回路,并不能系统地研究这些大脑区域的神经信号如何成为学习的基础。因此,我们对神经计算的理解与不确定性下策略发现的长期过程之间仍然存在很大差距。这一建议将结合新颖的行为任务和先进的神经回路工具来填补这一空白。我们将纵向测量和操纵从naïve到专家表现的神经信号,在小鼠学习一个允许多种行为策略的视觉决策任务中,并将使用计算工具形式化神经活动和学习动态之间的关系。这项工作将解决这些问题:1。在不确定性条件下,学习过程中神经信号是如何形成的,与行为策略有何关系?在学习过程中,纹状体和额叶皮层释放的DA编码了什么?不同脑区的DA在学习过程中是否起着不同的因果作用?纹状体和额叶皮层的神经信号如何发展并与学习过程中的行为策略相关?这项工作将纠正学习的神经基础数据的缺乏,使我们对感知不确定性下调节学习的神经回路的理解发生了一步变化。
英文摘要
How does the brain learn to make efficient decisions in an uncertain world? A hallmark of biological learning is strategy discovery: over long periods of time, animals can transition through a range of behaviors on their way from naïve to expert performance. Decades of research have highlighted the role of midbrain dopamine (DA) neurons as well as major DA-receiving brain regions, striatum and frontal cortex, in learning. However, previous studies did not probe these neural circuits from the very first trial of the task, while animals test and select among possible strategies for gaining reward under perceptual uncertainty. Moreover, past studies often examined these neural circuits at a small scale and in isolation, and could not systematically investigate how neural signals across these brain regions underlie learning. A large gap thus remains between our understanding of neural computations and the long-term process of strategy discovery under uncertainty. This proposal will combine novel behavioral tasks and advanced neural circuit tools to fill this gap. We will longitudinally measure and manipulate neural signals from naïve to expert performance in mice learning a visual decision task that admits multiple behavioral strategies, and will use computational tools to formalize the relation between neural activity and learning dynamics. The work will address these questions:1. How do DA neural signals develop during learning under uncertainty, and how do they relate to behavioral strategies?2. What does DA release across striatum and frontal cortex encode during learning, and does DA in different brain regions play distinct causal roles in learning?3. How do neural signals across striatum and frontal cortex develop and relate to behavioral strategies during learning?This work will rectify the paucity of data on the neural bases of learning, generating a step change in our understanding of neural circuits regulating learning under perceptual uncertainty.
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