CAREER: Probabilistic inference in the primate visual system

本研究旨在探讨知觉推理过程中感觉和前额叶皮层之间的功能关系（即，推断感觉环境的属性）和感知内省（即，评估该推断的质量）。这两项任务的关键在于，它们都需要考虑感知的不确定性。当感知不确定性高时（即，当感觉测量不明确时），感知推断往往受先前经验的指导，并且感知指导决策的置信度往往较低。神经回路如何评估感觉信号的可靠性，并将这种评估用于感知推理和感知内省，目前还没有很好的理解。拟议中的研究将从两个方面促进我们的理解。首先，通过同时记录动物执行知觉推理和内省任务的早期视觉皮层和下游联想皮层的神经活动。其次，通过使用这些经验观察来评估不同大脑区域如何在面对不确定性时协同工作以产生感知和认知的理论建议。将收集的数据和将开发的数据分析工具将提供给神经科学界。该项目还将产生更广泛的影响，通过提供一组来自代表性不足背景的初级和高级学生在UT奥斯汀的计算和系统神经科学的带薪暑期研究实习，并通过参与德克萨斯州中部的初中和高中学生在神经科学。该项目将研究概率感知推理的神经实现，使用灵长类动物V1及其在前额叶皮层（区域FEF）中的下游靶点作为模型系统。该提案采用行为任务，其中非人类灵长类动物用扫视眼球运动传达刺激方向判断（以及他们对这一决定的信心）。任务范式将知觉推理和知觉信心的神经相关物与行动计划的神经相关物区分开来。将使用多个共同插入的多电极阵列记录神经群体活动。该提案利用神经编码的功能模型来研究V1和FEF群体活动之间关系的性质，强度和动态演变。实验涉及操纵的刺激功能，感官的不确定性，和先验概率。因此，该项目旨在揭示在许多大脑区域和模式中实例化的神经编码的规范原则。所收集的数据将是有价值的大规模分布式计算模型的发展，寻求桥接电路结构和功能。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project investigates the functional relation between sensory and prefrontal cortex during perceptual inference (i.e., inferring properties of the sensory environment) and perceptual introspection (i.e., evaluating the quality of this inference). Key to both tasks is that they require consideration of perceptual uncertainty. When perceptual uncertainty is high (i.e., when sensory measurements are ambiguous), perceptual inferences tend to be guided by prior experience, and confidence in perceptually guided decisions tends to be low. How neural circuits assess the reliability of sensory signals and use this assessment for perceptual inference and perceptual introspection is not well understood. The proposed research will advance our understanding in two ways. First, by simultaneously recording neural activity in early visual cortex and down-stream association cortex from animals performing perceptual inference and introspection tasks. Second, by using these empirical observations to evaluate theoretical proposals of how different brain regions work together to produce perception and cognition in the face of uncertainty. The data that will be collected and the data-analysis tools that will be developed will be made available to the neuroscience community. This project will also have a broader impact by providing a select group of junior and senior students from underrepresented backgrounds with a paid summer research internship in computational and systems neuroscience at UT Austin, and by engaging middle- and high-school students of central Texas in neuroscience.This project will study the neural implementation of probabilistic perceptual inference, using primate V1 and its downstream targets in the prefrontal cortex (area FEF) as the model system. The proposal employs behavioral tasks in which non-human primates communicate stimulus orientation judgements (and their confidence in this decision) with saccadic eye movements. The task paradigms distinguish the neural correlates of perceptual inference and perceptual confidence from those of action planning. Neural population activity will be recorded with multiple jointly inserted multi-electrode arrays. The proposal leverages functional models of neural coding to study the nature, strength, and dynamic evolution of the relation between V1 and FEF population activity. The experiments involve manipulations of stimulus features, sensory uncertainty, and prior probability. As such, the project aims to uncover canonical principles of neural coding that are instantiated in many brain regions and modalities. The data collected will be valuable for the development of models of large-scale distributed computation that seek to bridge circuit structure and function.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.