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.

当我们与环境互动时，我们需要评估我们的决定的潜在后果。然而，我们的决定通常是基于不完整的信息，并且可能以不同的方式模糊不清。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，我们可能对钥匙放在哪里或上次丢失时在哪里找到它们的记忆不完整。各种模式（感知、运动、认知）中的这种不确定性提出了大脑必须应对的根本挑战，以确保行为保持适应环境的限制和要求。在这个跨学科的项目中，我们建议将神经生理学家（Rochefort实验室）和计算神经科学家（Lengyel实验室）的专业知识结合起来，以解决神经科学中的这个关键问题：大脑如何编码不确定性？大量的文献支持这样一种观点，即人类和其他动物在广泛的认知领域（从感官知觉到运动规划和执行）中根据贝叶斯决策规则（至少近似地）表示和传播不确定性来做出决策。然而，这些计算背后的实际神经元表示仍然未知。特别是，目前还不清楚大脑中的不确定性是否是机会主义地表示的，即仅关于决策变量（决策过程的最后阶段的变量），或者它是否在信息处理的不同阶段无处不在，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，这两种策略都有互补的优势和劣势。在解决大脑中的概率表示是机会主义的还是普遍存在的问题上，一个基本的限制因素是缺乏实验范式来区分决策不同阶段的不确定性（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行为和神经数据记录在一个范例，已专门设计来解决这个challenge.We解决这个问题的感知决策的背景下，使用小鼠初级视觉皮层（V1）作为模型系统。我们将使用Rochefort实验室获得的数据集，其中在V1中记录了大神经元群体的活动，而小鼠正在执行视觉引导的目标导向行为任务。在训练动物完成任务后，我们通过修改视觉刺激特性来操纵动物的感官不确定性。该项目围绕两个目标组织：-目标1。分析行为数据，以获得知觉和决策不确定性的试验措施。目标2.确定小鼠V1中感知与决策不确定性的神经元表示。该项目将使神经生理学（Rochefort）和计算神经科学（Lengyel）实验室之间建立强大的跨学科合作关系。这项合作将推动研究全面了解成人大脑在细胞，网络和行为水平上感知不确定性的编码。它还将导致开发新的分析工具和新的计算模型，用于神经元在大脑中执行决策。通过整合来自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新的跨学科专业知识，这在这一研究领域具有很高的需求。