CAREER: Bridging dynamical and statistical models of neural circuits -- a mechanistic approach to multi-spike synchrony

职业：桥接神经回路的动力学和统计模型——多尖峰同步的机械方法

• 批准号: 1056125
• 负责人: Eric Shea-Brown
• 金额: $ 46.91万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1056125&HistoricalAwards=false
• 关键词: CAREER; Bridging; dynamical; statistical; models

Questions of neural synchrony -- correlations in cell-to-cell spiking -- have driven decades of research. However, recent technological and theoretical advances have thrust open two lines of inquiry. The first is understanding the combinatorial scale of the correlations that occur in natural and model neural networks. It is well known that describing neural activity requires pairwise statistical interactions -- but we do not yet understand when network dynamics produce patterns of correlations that extend beyond this pairwise description, when the pairwise descriptions will be complete, and what the overall implications are for neural coding and signal processing. The Principal Investigator will address these questions for a set of "canonical" neural circuits, or motifs, and will build toward networks of gradually increasing complexity in their dynamics and architecture. Further, extending beyond intrinsic network dynamics, he will ask how these basic network properties determine the ways in which patterns of synchrony can be controlled by external stimulation. Answering these complementary questions requires synergies between methods of stochastic processes, dynamical systems, and statistical inference.How do how networked neurons work together to produce the brain's astonishing computational ability? Such coordinated neural dynamics are characterized by synchrony among different neurons. One prospect is that this coordinated activity opens new channels for signal processing: there is a combinatorial explosion in the number of possible multi-neuron patterns that can occur in increasingly large networks. However, we only have the first hints at whether and when these patterns systematically occur in the brain's networks, and what information they might (or might not) carry. Shea-Brown will study the fundamental properties of neural dynamics, connectivity, and noise that determine the level and impact of multi-neuron synchrony in a series of networks of gradually increasing complexity. He will use interdisciplinary tools from both deterministic and statistical branches of applied mathematics to understand how levels of synchrony are created, destroyed, and manipulated by external stimulation. These findings will contribute to experimental and clinical neuroscience: working in collaboration with experimentalists, the investigator will make predictions for light stimuli that evoke higher-order correlations in the retina, and for electrical stimuli that suppress pathological synchrony in neurodegenerative disease. These questions, as part of theoretical neuroscience -- an emerging field that is rich in open questions and highly varied interdisciplinary techniques -- present a strong opportunity for recruiting, engaging, and training undergraduates in the mathematical sciences. Shea-Brown will direct this opportunity toward the underrepresented groups from which new scholars are most urgently needed, through an integrated four-year research pathway for undergraduates. This will be developed together with newly designed units in the computational science and mathematical biology courses taught by the investigator.

神经同步性的问题--细胞与细胞之间的相关性--已经推动了几十年的研究。 然而，最近的技术和理论进步已经打开了两条调查路线。 第一个是理解自然和模型神经网络中发生的相关性的组合规模。 众所周知，描述神经活动需要成对的统计相互作用，但我们还不知道网络动力学何时产生超出这种成对描述的相关模式，成对描述何时完成，以及对神经编码和信号处理的总体影响。 主要研究者将解决这些问题的一组“规范”的神经回路，或图案，并将建立逐步增加其动态和架构的复杂性的网络。 此外，除了内在的网络动力学之外，他还将探讨这些基本的网络特性如何决定外部刺激控制同步模式的方式。 解决这些互补的问题需要随机过程、动力系统和统计推断方法之间的协同作用。网络化的神经元是如何协同工作以产生大脑惊人的计算能力的？ 这种协调的神经动力学的特征在于不同神经元之间的同步。 一个前景是，这种协调的活动为信号处理开辟了新的渠道：在越来越大的网络中，可能出现的多神经元模式的数量会出现组合爆炸。 然而，对于这些模式是否以及何时系统地出现在大脑网络中，以及它们可能（或可能不）携带什么信息，我们只有初步的线索。 Shea-Brown将研究神经动力学，连通性和噪声的基本特性，这些特性决定了一系列逐渐增加复杂性的网络中多神经元同步的水平和影响。 他将使用应用数学的确定性和统计学分支的跨学科工具来理解同步水平是如何被外部刺激创造、破坏和操纵的。 这些发现将有助于实验和临床神经科学：与实验学家合作，研究人员将预测引起视网膜高阶相关性的光刺激，以及抑制神经退行性疾病病理同步性的电刺激。 这些问题，作为理论神经科学的一部分-一个新兴的领域，是丰富的开放性问题和高度多样化的跨学科技术-提供了一个强大的机会，招聘，从事和培训数学科学的本科生。 Shea-Brown将通过为本科生提供的四年综合研究途径，为最迫切需要新学者的代表性不足的群体提供这个机会。 这将与研究人员教授的计算科学和数学生物学课程中新设计的单元一起开发。