CAREER: Unlocking the neural code with spikes, currents and conductances

职业：用尖峰、电流和电导解锁神经代码

• 批准号: 1601115
• 负责人: Jonathan Pillow
• 金额: $ 38.8万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1601115&HistoricalAwards=false
• 关键词: CAREER; Unlocking; neural; code; spikes

This project aims to develop new mathematical and computational tools for understanding the basic information-processing strategies of neurons and neural populations in the brain. Recent technological advances have enabled large-scale recordings of neural activity from intact neural circuits, but there is a severe shortage of theoretical methods for revealing what this activity means--that is, what information it carries, and how it gives rise to behavior. The research described in this proposal will address these questions using novel statistical techniques for studying the neural code in single neurons and neural populations, using both extracellularly and intracellularly recorded neural data. There are at least two important statistical aspects to the proposed research: first, new methods for reliably estimating the neurobiological variables of interest (e.g., spikes, membrane currents, synaptic conductances, etc.) from noisy experimental recordings; and second, powerful, flexible, model-based methods for understanding the complex, high-dimensional, and time-dependent relationship between sensory stimuli, behavioral responses, and neural activity. The three specific aims of the proposal focus on: (1) the encoding and decoding of decisions from multi-neuron spike trains in parietal cortex; (2) intracellular signals in visual cortex, at the level of membrane potential and synaptic currents, and their relationship to the information conveyed in spike trains; and (3) advanced methods for adaptive, "closed loop" neurophysiology experiments, leading to more informative experimental designs and more interpretable neural datasets. All three aims will involve intensive collaborations with experimental groups and will tightly integrate theory and experiment.The proposed research will reveal new features of visual and cognitive representations in cortex, and will unlock the neural code at multiple levels of biophysical detail in sensory, motor and cognitive systems. More broadly, the research will shed new light on information flow in groups of neurons, with implications for both the treatment of brain disorders and the design of new technology.

该项目旨在开发新的数学和计算工具，以了解大脑中神经元和神经群的基本信息处理策略。最近的技术进步使得从完整的神经回路中大规模记录神经活动成为可能，但严重缺乏理论方法来揭示这种活动意味着什么——也就是说，它携带了什么信息，以及它如何引起行为。本提案中描述的研究将使用新的统计技术来研究单个神经元和神经群体中的神经密码，使用细胞外和细胞内记录的神经数据来解决这些问题。提出的研究至少有两个重要的统计方面：首先，从嘈杂的实验记录中可靠地估计感兴趣的神经生物学变量（例如，尖峰，膜电流，突触电导等）的新方法；第二，强大、灵活、基于模型的方法，用于理解感官刺激、行为反应和神经活动之间复杂、高维和时间依赖的关系。该提案的三个具体目标集中在：(1)顶叶皮层多神经元尖峰序列的决策编码和解码；(2)在膜电位和突触电流水平上，视觉皮层的胞内信号及其与脉冲传递信息的关系；(3)先进的自适应“闭环”神经生理学实验方法，带来更多信息丰富的实验设计和更可解释的神经数据集。所有这三个目标都将涉及与实验小组的密切合作，并将理论与实验紧密结合。这项研究将揭示皮层中视觉和认知表征的新特征，并将在感觉、运动和认知系统的生物物理细节的多个层面上解开神经密码。更广泛地说，这项研究将为神经元群中的信息流提供新的线索，对大脑疾病的治疗和新技术的设计都有意义。