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: 1150186
• 负责人: Jonathan Pillow
• 金额: $ 54.65万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1150186&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体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，但严重缺乏揭示这一活动含义的理论方法--即，它携带什么信息，以及它是如何引发行为的。这项建议中描述的研究将使用新的统计技术来解决这些问题，利用细胞外和细胞内记录的神经数据来研究单个神经元和神经群体中的神经编码。这项拟议的研究至少有两个重要的统计学方面：第一，可靠地估计感兴趣的神经生物学变量(例如，棘波、膜电流、突触电导等)的新方法。第二，强大的、灵活的、基于模型的方法，用于理解感觉刺激、行为反应和神经活动之间的复杂、高维和时间依赖关系。该提案的三个具体目标集中在：(1)对顶叶皮质多个神经元棘波序列的决策进行编码和解码；(2)在膜电位和突触电流水平上的视觉皮质细胞内信号及其与棘波序列所传达信息的关系；以及(3)自适应“闭环”神经生理学实验的先进方法，从而产生更丰富的实验设计和更可解释的神经数据集。这三个目标都将涉及与实验小组的密集合作，并将理论和实验紧密结合。拟议的研究将揭示大脑皮质视觉和认知表征的新特征，并将在感觉、运动和认知系统的多个生物物理细节水平上解锁神经密码。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。