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体中出现的活动模式以及将这些模式与神经系统的功能联系起来的兴趣激增。 然而，这些人群接收到的各种不同的感官输入以及这些输入引起的各种不同的反应，使得仅凭经验观察无法实现这一目标。由于神经网络动力学的非线性，这一挑战变得更加复杂，这使得很难通过从更简单的系统的观察中推断来预测活动模式。 因此，需要预测数学建模和更深入地了解神经元回路的动力学。 在NSF以前的支持下，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。 他们通过将基础数学理论扩展到更复杂和现实的网络来建立这些结果。 使用这种方法，研究人员团队研究了集体活动是如何由网络结构，细胞动力学和刺激驱动控制的一组神经网络，这些神经网络代表了整个神经系统的结构。解决这些问题将为当代生物学应用打开大门，并将迎接实验神经科学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，这些神经网络不能分解为孤立的部分。通过与大量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引入和测试。 从长远来看，在集体网络动力学存在的情况下编码的结果将影响神经假体的设计，神经假体通过皮层、视网膜和丘脑植入物编码感觉信号。

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.