The Mechanics of Neural Variability

神经反应的一个明显而重要的特征是在感觉和运动任务中从一个试验到另一个试验有很大程度的变异性。神经变异性是可塑性的，取决于任务细节、认知状态和感觉输入；然而，人们对调节这种变异性的机制知之甚少。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性力学理论。最近的大脑皮层实验表明，自发神经活动的随机动力学非常丰富，超过了在诱发或任务驱动反应中观察到的。这项研究将展示集群式神经架构如何复制这一发现。这项研究将从皮质组装动力学的简化马尔可夫链模型中发展出关键的见解，这将使我们以前基于模拟的研究有更正式的方法。揭示神经可变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。与之形成鲜明对比的是，大脑动力学显示了神经反应的相当大的试验到试验的可变性，这清楚地表明神经系统的工作原理与硅机器不同。本研究将运用统计力学和非线性系统理论的现代技术，为神经变异性的力学提供理论基础。具体地说，我们的理论将揭示大脑的潜在电路如何参与产生和控制神经变异性。这项研究将指导未来旨在更好地表征神经电路的实验，将任何数据放在神经系统核心功能特征的背景下。此外，我们的研究将为许多神经退行性疾病提供关键的见解，在这些疾病中，常见的神经特征是变异性过大。

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.