Neural Basis of Magnitude Representations and Metric Error Monitoring in Mice

小鼠幅度表示和度量误差监测的神经基础

• 批准号: RGPIN-2021-03334
• 负责人: BALCI, FUAT
• 金额: $ 3.42万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=744694
• 关键词: Neural; Basis; Magnitude; Representations; Metric

Interval timing is the ability to keep track of intervals in seconds to minutes range while counting is the ability to enumerate discrete events. The timing and counting behaviors have common variability features, suggesting that timing/counting mechanisms operate with similar processing dynamics and may even originate from a common neural locus. Humans and non-human animals account for this variability during decision-making (CCV 32, 45, 49, 62), suggesting that the endogenous uncertainty about time/counts is cognitively accessible. In support of this claim, I recently discovered that humans can monitor the direction and size of temporal, numerical, and spatial errors (e.g. CCV 5, 20, 42). This discovery (metric error monitoring - MEM) provided a new theoretical basis for the long-term (20 yrs) objective of my research program; namely the elucidation of how the brain quantifies experiences and errors in performance based on my unique combination of behavioral, computational, and neuroscientific methods. Some of my short-term objectives (5 yrs) that will lay the foundation for my long-term objective are 1) Investigation of MEM in mice: I will train mice to keep a lever depressed for a minimum duration or until a minimum number of signals is experienced. I will infer judgments about the errors in timing/counting performance based on response rates during the reward anticipation period following the release of the lever. I will also examine how metric error judgments are translated into subsequent behavioral adjustments. 2) Investigation of the neural basis of MEM in mice using optogenetics: I hypothesize that in this task responses that are earlier and later than the target magnitude are signalled by mesocortical negative and positive dopaminergic reward prediction error (dRPE), respectively and that the dRPE amplitude codes for the proximity of the performance to the target. 3) Examining the mechanistic overlap between timing and counting in mice to inform an overarching neurocomputational account of magnitude processing: I will test whether the optogenetic modulation of the activity of dopaminergic neurons in substantia nigra pars compacta biases temporal and numerical judgments similarly. By examining metric error judgments about induced biases, I will also test my model-based assertion that MEM is derived from the independent processing of magnitudes in the perceptual and motor systems (CCV 42). My NSERC program buttressed by these aims will lead to a comprehensive and overarching understanding of the quantification capacity of the brain based on causal evidence with an expected transformative impact on the conceptualization of magnitude representations and error processing in behavioral neuroscience and cognitive science. I will train HQPs in cutting-edge behavioral, computational, and neuroscience methods and their integration for studying different functions of the brain, and securing impactful research positions in and outside academia.

间隔计时是跟踪以秒到分钟为单位的间隔的能力，而计数是枚举离散事件的能力。计时和计数行为具有共同的可变性特征，这表明计时/计数机制以类似的处理动态操作，甚至可能起源于共同的神经位点。人类和非人类动物在决策过程中解释了这种变异性（CCV 32，45，49，62），这表明关于时间/计数的内源性不确定性是可认知的。为了支持这一观点，我最近发现人类可以监控时间、数值和空间误差的方向和大小（例如CCV 5，20，42）。这一发现（度量误差监测- MEM）为我的研究计划的长期（20年）目标提供了新的理论基础;即基于我独特的行为，计算和神经科学方法的组合，阐明大脑如何量化经验和错误。我的一些短期目标（5年）将为我的长期目标奠定基础：1）在小鼠中研究MEM：我将训练小鼠在最短时间内保持杠杆压下，或直到经历最少数量的信号。我将根据杠杆释放后奖励预期期内的反应率来推断对计时/计数性能错误的判断。我还将研究度量错误判断如何转化为随后的行为调整。2)使用光遗传学在小鼠中的MEM的神经基础的调查：我假设，在该任务中，早于和晚于目标幅度的响应分别由中皮层负性和正性多巴胺能奖励预测误差（dRPE）发出信号，并且dRPE幅度编码接近目标的性能。3)研究小鼠中计时和计数之间的机械重叠，以告知对幅度处理的总体神经计算账户：我将测试黑质多巴胺能神经元活动的光遗传学调制是否类似地偏向时间和数值判断。通过检查度量错误的判断引起的偏见，我也将测试我的基于模型的断言，MEM是来自独立的处理的幅度在知觉和运动系统（CCV 42）。在这些目标的支持下，我的NSERC计划将导致对基于因果证据的大脑量化能力的全面和全面的理解，并对行为神经科学和认知科学中的幅度表示和错误处理的概念化产生预期的变革性影响。我将在尖端的行为，计算和神经科学方法及其整合方面培训HQP，以研究大脑的不同功能，并在学术界内外获得有影响力的研究职位。