Collaborative Research: Relating Architecture, Dynamics, and Temporal Correlations in Networks of Spiking Neurons

新的记录方法使研究人员能够以前所未有的范围和细节探测神经活动的结构。因此，人们对理解整个神经元群中出现的活动模式以及将这些模式与神经系统功能联系起来的兴趣激增。然而，这些人群接收到的不同感官输入的压倒性范围-以及这些输入引起的巨大范围的不同反应-使得仅根据经验观察无法实现这一目标。这一挑战由于神经网络动力学的非线性而变得更加复杂，这使得通过从简单系统的观察推断来预测活动模式变得困难。因此，预测数学建模和对神经元回路动力学的更深入理解是必要的。在NSF之前的支持下，研究人员在统计、随机分析和非线性动力学的界面上开发了数值和分析工具，以了解简单但基本的微电路中相关性的起源和影响。他们在这些结果的基础上，将基础数学理论扩展到更复杂、更现实的网络。使用这种方法，研究小组研究了集体活动是如何由网络结构、细胞动力学和一组神经网络中的刺激驱动控制的，这些神经网络代表了整个神经系统的结构。回答这些问题将打开当代生物学应用的大门，并将满足实验神经科学中最近技术发展所带来的关键理论挑战。关键的创新在于理解大型神经网络的集体动力学，这些网络不能被分解成它们孤立的部分。通过与广泛的实验合作者的持续互动，这些想法被广泛的神经科学家社区介绍和测试。从长远来看，在集体网络动力学存在下的编码结果将影响神经假肢的设计，神经假肢通过皮质、视网膜和丘脑植入物对感觉信号进行编码。

New recording methods allow researchers to probe the structure of neural activity with unprecedented scope and detail. As a result there is an explosion of interest in understanding the patterns of activity that emerge in entire neuronal populations and relating these patterns to the function of the nervous system. However, the overwhelming range of different sensory inputs that these populations receive -- and the vast range of different responses that these inputs evoke -- make it impossible to achieve this goal based on empirical observations alone. This challenge is compounded due to the nonlinearity of neuronal network dynamics, which makes it difficult to predict patterns of activity by extrapolation from observations of simpler systems. Predictive mathematical modeling and a deeper understanding of the dynamics of neuronal circuits is therefore required. With previous NSF support, the investigators developed numerical and analytic tools at the interface of statistics, stochastic analysis and nonlinear dynamics, to understand the genesis and impact of correlations in simple, but fundamental microcircuits. They build on these results by extending the underlying mathematical theory to more complex and realistic networks. Using this approach, the team of researchers examines how collective activity is controlled by network architecture, cell dynamics, and stimulus drive in a set of neural networks that typify structures across the nervous system.Answering these questions will open the door to contemporary biological applications and will meet key theoretical challenges posed by recent technological developments in experimental neuroscience. The key innovation lies in the understanding the collective dynamics of large neural networks that cannot be decomposed into their isolated parts. Through continued interactions with a broad set of experimental collaborators, these ideas are introduced and tested by a broad community of neuroscientists. In the longer term, results on coding in the presence of collective network dynamics will impact the design of neural prosthetics, which code sensory signals via cortical, retinal, and thalamic implants.