The Mechanics of Neural Variability

神经变异的机制

• 批准号: 1313225
• 负责人: Brent Doiron
• 金额: $ 27.5万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1313225&HistoricalAwards=false
• 关键词: Mechanics; Neural; Variability

A clear and important signature of neural response is the large degree of variability from trial-to-trial in sensory and motor tasks. Neural variability is malleable depending upon task specifics, cognitive state, and sensory input; however little is known about the mechanisms that mediate this variability. Using techniques from non-equilibrium statistical mechanics and nonlinear systems theory this project will build a coherent theory of the mechanics of neural variability. Recent experiments across cortex show that the stochastic dynamics of spontaneous neural activity is very rich, beyond that observed during evoked or task driven responses. This research will show how clustered neural architectures can replicate this finding. This research will develop key insights from simplified Markov chain models of cortical assembly dynamics, which will allow a more formal approach to our previous simulation based studies. Uncovering the core mechanics of neural variability is a critical step in giving a foundation for a theory of neural computation. Modern computers minimize noisy fluctuations in transistors and semiconductors in order to improve performance reliability. In stark contrast, brain dynamics show a sizable trial-to-trial variability of neural responses, making it clear that the nervous system works under different principles than silicon machines. This research will use contemporary techniques from statistical mechanics and nonlinear system theory to give a theoretical foundation to the mechanics of neural variability. Specifically, our theory will expose how the underlying circuitry of the brain is involved in producing and controlling neural variability. This research will guide future experiments that aim to better characterize neural circuits, placing any data in the context of a core functional feature of the nervous system. Furthermore, our research will give critical insights concerning many neurodegenerative diseases where a common neural signature is an excess of variability.

神经反应的一个明确而重要的特征是，在感觉和运动任务中，试验与试验之间存在很大程度的差异。 神经变异性是可塑性的，这取决于任务的具体情况，认知状态和感觉输入，但很少有人知道的机制，介导这种变异性。 利用非平衡统计力学和非线性系统理论的技术，这个项目将建立一个连贯的神经变异力学理论。最近的跨皮层实验表明，自发神经活动的随机动力学是非常丰富的，超出了在诱发或任务驱动的反应期间观察到的。 这项研究将展示集群神经架构如何复制这一发现。 这项研究将开发关键的见解，从简化的马尔可夫链模型的皮质组装动力学，这将允许一个更正式的方法，我们以前的模拟为基础的研究。 揭示神经变异性的核心机制是为神经计算理论奠定基础的关键一步。 现代计算机将晶体管和半导体中的噪声波动降至最低，以提高性能可靠性。与此形成鲜明对比的是，大脑动力学显示了神经反应的相当大的试验间差异，这清楚地表明神经系统的工作原理与硅机器不同。 本研究将使用统计力学和非线性系统理论的当代技术，为神经变异机制提供理论基础。 具体来说，我们的理论将揭示大脑的底层电路是如何参与产生和控制神经变异的。 这项研究将指导未来旨在更好地表征神经回路的实验，将任何数据置于神经系统核心功能特征的背景下。 此外，我们的研究将提供有关许多神经退行性疾病的重要见解，其中常见的神经特征是过度的变异性。