Collaborative Research: Correlations in neural dynamics and coding

合作研究：神经动力学和编码的相关性

• 批准号: 0817649
• 负责人: Kresimir Josic
• 金额: $ 15.11万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0817649&HistoricalAwards=false
• 关键词: Collaborative; Research; Correlations; neural; dynamics

Multi-electrode recording technology and voltage sensitive dyes allow researchers to probe the structure of correlated, stimulus-driven neural activity in groups of cells. However, the diversity of brain areas and stimuli make a complete sampling of these patterns and their effects impossible. Furthermore, the evidence of correlations in stimulus response is strong, yet its role in neural coding difficult to intuit. Therefore, a combined, predictive theory of correlation formation and impact is required. This challenge is approached in three stages. First, a general mathematical theory is developed that relates input correlations of a stochastic forcing to the output correlations of resultant spike trains. The underlying tools will be linear response, population density, and Monte-Carlo methods for the nonlinear stochastic differential equations of spiking neural circuits. Next, this theory is applied to a variety of neural models to quantify how neuron biophysics, morphology, and coupling influence input-output correlation transfer. Finally, information-theoretic analyses are performed to estimate the impact of spike train correlations on the encoding and propagation of sensory inputs in representative neural circuits. Throughout the project, the investigators will work with experimental collaborators to refine and test these predictions. Understanding the mechanisms by which the nervous system represents and processes information is a fundamental challenge for mathematical biology. It has long been known that information is represented by the intensity of individual neurons' responses. However, new multi-neuron recording and brain imaging techniques are revealing that the information carried by neural tissue is much more (or much less) than the summed contributions of individual neurons. In other terms, the cooperative, correlated features of neural responses can be essential. This poses a pair of fundamental, but unresolved theoretical questions: What are the basic mechanisms by which correlated activity is generated and propagated through layers of neural tissue? What are the consequences for information processing in neuronal networks? The answers will, in stages, make predictions for ongoing neurobiological experiments. For instance, understanding the relation between correlations and neural coding stands to impact the design of neural prosthetics, which code motor and sensory signals via cortical, retinal, thalamic, and cochlear implants. From an alternative perspective, many neurological disorders, such as epilepsy and Parkinson's disease, involve excessive correlation in neural tissue--describing the genesis of correlations and its negative impact on neural coding will aid in designing appropriate treatments that ultimately reduce correlation in the nervous system. Along the way, graduate students involved in this research will receive training in a highly interdisciplinary field, and will gain a broad perspective on mathematical neuroscience through regular visits between three research groups in different regions of the United States. The active involvement of the investigators in undergraduate research and course development will provide an opportunity to translate the questions addressed here into compelling educational topics on the cooperative activity in neural networks that will be accessible to a wide audience.

多电极记录技术和电压敏感染料使研究人员能够探测细胞群中相关的刺激驱动的神经活动的结构。然而，大脑区域和刺激的多样性使得对这些模式及其影响的完整采样成为不可能。此外，刺激反应中的相关性的证据是强有力的，但它在神经编码中的作用难以直观。因此，需要一种组合的、预测相关性形成和影响的理论。这一挑战分三个阶段进行。首先，一个一般的数学理论，涉及输入相关的随机强迫的输出相关的穗列车。基本的工具将是线性响应，人口密度，和蒙特-卡罗方法的非线性随机微分方程的尖峰神经电路。接下来，将该理论应用于各种神经模型，以量化神经元生物物理学、形态学和耦合如何影响输入-输出相关性转移。最后，信息理论分析进行估计的影响，尖峰序列的相关性的编码和传播的感官输入在代表性的神经回路。在整个项目中，研究人员将与实验合作者合作，以完善和测试这些预测。理解神经系统表示和处理信息的机制是数学生物学的一个基本挑战。人们早就知道，信息是由单个神经元反应的强度来表示的。然而，新的多神经元记录和大脑成像技术揭示了神经组织携带的信息比单个神经元的贡献总和要多得多（或少得多）。换句话说，神经反应的合作、相关特征可能是必不可少的。这就提出了两个基本但尚未解决的理论问题：相关活动产生并通过神经组织层传播的基本机制是什么？神经元网络中的信息处理有什么后果？这些答案将分阶段为正在进行的神经生物学实验做出预测。例如，理解相关性和神经编码之间的关系将影响神经假体的设计，神经假体通过皮层、视网膜、丘脑和耳蜗植入物编码运动和感觉信号。从另一个角度来看，许多神经系统疾病，如癫痫和帕金森病，涉及神经组织中的过度相关性-描述相关性的起源及其对神经编码的负面影响将有助于设计最终减少神经系统相关性的适当治疗方法。沿着，参与这项研究的研究生将接受高度跨学科领域的培训，并将通过美国不同地区的三个研究小组之间的定期访问，获得数学神经科学的广阔视角。研究人员积极参与本科研究和课程开发将提供一个机会，将这里讨论的问题转化为令人信服的教育主题，在神经网络中的合作活动，将可供广大观众。