Neural Representation of Uncertainty in Mouse Visual Cortex

小鼠视觉皮层不确定性的神经表征

• 批准号: BB/X013308/1
• 负责人: Nathalie Louise Isabelle Rochefort
• 金额: $ 25.59万
• 依托单位: University of Edinburgh
• 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
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX013308%2F1
• 关键词: Neural; Representation; Uncertainty; Mouse; Visual

When interacting with our environment, we need to evaluate the potential consequences of our decisions. However, our decisions are usually based on information that is partial and may be ambiguous in different ways. For example, when we are looking for our keys in the flat, we have to take into account that the keys may be partially or fully occluded, we may have imperfect memory about where we put them, or where we found them last time we lost them. Such uncertainty in various modalities (perceptual, motor, cognitive) presents a fundamental challenge that the brain must tackle in order to ensure that behaviour remains adapted to the constraints and demands of the environment. In this interdisciplinary project, we propose to bring together the expertise of neurophysiologists (Rochefort lab) and computational neuroscientists (Lengyel lab) to tackle this critical question in neuroscience: how does the brain encode uncertainty?A rich body of literature supports the notion that humans and other animals make decisions based on representing and propagating uncertainty (at least approximately) according to the rules of Bayesian decision making, in a broad range of cognitive domains (from sensory perception to motor planning and execution). However, the actual neuronal representations underlying these computations remain unknown. In particular, it is unclear whether uncertainty in the brain is represented opportunistically, i.e. only about decision variables (variables at the final stages of the decision-making process), or whether it is represented ubiquitously, throughout different stages of information processing, including perceptual uncertainty, memory uncertainty, as well as over variables relevant to the decision. In artificial intelligence, there are examples for both strategies, with complementary advantages and disadvantages.A fundamental factor limiting progress in resolving whether probabilistic representations in the brain are opportunistic or ubiquitous is the lack of experimental paradigms that can distinguish between uncertainty at different stages of decision making (e.g. uncertainty about perceptual vs. decision variables). Thus, the main goal of the project is to analyse behavioural and neural data recorded during a paradigm that has been specifically designed to address this challenge.We address this question in the context of perceptual decision making, using the mouse primary visual cortex (V1) as a model system. We will use a dataset obtained in the Rochefort lab, in which the activity of large neuronal populations was recorded in V1 while mice were performing a visually-guided goal-directed behavioural task. After training animals on the task, we manipulated the sensory uncertainty of the animals by modifying visual stimulus properties. The project is organised around 2 aims:- Aim 1. Analyse behavioural data in order to obtain trial-by-trial measures of perceptual and decision uncertainty.- Aim 2. Determine the neuronal representation of perceptual vs. decision uncertainty in mouse V1.This project will enable a strong interdisciplinary partnership between a neurophysiology (Rochefort) and a computational neuroscience (Lengyel) lab. This collaboration will drive research to comprehensively understand the encoding of perceptual uncertainty in the adult brain at the cellular, network, and behavioural level. It will also lead to the development of new analysis tools and new computational models of the neuronal implementation of decision-making in the brain. By integrating the skills and expertise from an experimental and a computational lab, members of both labs will develop new interdisciplinary expertise that is of high demand in this field of research.

当与环境互动时，我们需要评估我们的决定的潜在后果。然而，我们的决定通常基于不完整的信息，并且可能在不同方面是模糊的。例如，当我们在公寓里寻找钥匙时，我们必须考虑到钥匙可能部分或完全被遮挡，我们可能对我们把钥匙放在哪里，或者上次丢失钥匙时在哪里找到它们的记忆不完整。各种模式（知觉、运动、认知）的这种不确定性提出了大脑必须应对的基本挑战，以确保行为保持适应环境的约束和要求。在这个跨学科项目中，我们建议汇集神经生理学家（Rochefort 实验室）和计算神经科学家（Lengyel 实验室）的专业知识来解决神经科学中的这一关键问题：大脑如何编码不确定性？大量文献支持这样一种观点，即人类和其他动物根据贝叶斯决策规则基于表示和传播不确定性（至少近似）来做出决策， 在广泛的认知领域（从感官知觉到运动规划和执行）。然而，这些计算背后的实际神经元表征仍然未知。特别是，目前还不清楚大脑中的不确定性是否是机会主义的，即仅涉及决策变量（决策过程最后阶段的变量），还是在信息处理的不同阶段普遍存在，包括感知不确定性、记忆不确定性以及与决策相关的变量。在人工智能中，这两种策略都有例子，优点和缺点互补。在解决大脑中的概率表示是机会性的还是普遍存在的问题上，限制进展的一个基本因素是缺乏能够区分决策不同阶段的不确定性的实验范式（例如感知变量与决策变量的不确定性）。因此，该项目的主要目标是分析在专门为应对这一挑战而设计的范例中记录的行为和神经数据。我们使用小鼠初级视觉皮层（V1）作为模型系统，在感知决策的背景下解决这个问题。我们将使用在 Rochefort 实验室获得的数据集，其中当小鼠执行视觉引导的目标导向行为任务时，V1 中记录了大量神经元群的活动。在对动物进行任务训练后，我们通过修改视觉刺激特性来操纵动物的感官不确定性。该项目围绕 2 个目标进行组织：- 目标 1. 分析行为数据，以获得感知和决策不确定性的逐次试验测量。- 目标 2. 确定小鼠 V1 中感知与决策不确定性的神经元表征。该项目将使神经生理学 (Rochefort) 和计算神经科学 (Lengyel) 实验室之间建立强有力的跨学科伙伴关系。此次合作将推动研究在细胞、网络和行为层面全面了解成人大脑中感知不确定性的编码。它还将导致大脑决策神经元执行的新分析工具和新计算模型的开发。通过整合实验和计算实验室的技能和专业知识，两个实验室的成员将开发该研究领域急需的新的跨学科专业知识。