Building analysis tools and a theory framework for inferring principles of neural computation from multi-scale organization in brain recordings

总结 BRAIN计划正在为大脑记录提供突破性的技术， 将允许对神经活动的动力学进行独特的观察。然而，推断大脑 多通道生理记录的功能具有挑战性。一个关键的困难是 单个神经元和中观的，通常是有节奏的，细胞群相互作用， 复杂而反复的方式。这种复杂的神经元动力学很难分析， 很可能对大脑的功能很重要本提案将解决这一问题 通过开发（1）分析大脑活动的工具;（2）理论框架 用于表达底层计算和生成实验预测。 该项目的出发点是我们早期发现的相结构， 海马CA 1/CA 3区振荡局部场电位（LFP）进行定位 详细信息（Agarwal et al. 2014）。我们将发布软件工具， 提出了相位解码和提取有意义LFP分量的方法， 提供给更广泛的社区。此外，与实验室合作，我们 我将在海马体中研究这一发现的机制基础（Buzsaki 纽约大学，福斯特，加州大学伯克利分校），并探讨类似的方法如何利用阶段 皮质伽马振荡的多样性（Fries，MPI法兰克福）。研究目标是 开发分析工具，用于功能组件的解码和提取（目标1和 2），适用于海马和海马的广泛的多变量脑记录， 皮层活动 此外，我们将开发一个灵活的两级理论框架与软件工具（Aim 3)帮助神经科学家，特别是实验者， 研究中的大脑功能的计算，并建立一个具体的机制 这些计算的电路模型。计算描述级别将利用 向量符号架构的思想，一类连接主义模型，最初 提出了用于描述认知推理（Plate，1995; Kanerva，1996）。模型 由软件工具产生的将简要地封装关于 计算及其对大脑功能的实现，并产生预测， 可以在下一代的录音实验中进行测试。提出的理论 框架将在海马导航和 大脑皮层V1和V4区的视觉加工。

Summary The BRAIN initiative is enabling ground-breaking techniques for brain recordings that will permit a unique view onto the dynamics of neural activity. However, inferring brain function from multi-channel physiological recordings is challenging. A key difficulty is that individual neurons and mesoscopic, often rhythmic, cell populations interact in complicated and recurrent ways. Such complex neuronal dynamics is hard to analyze but very likely important to the functioning of the brain. This proposal will address this problem by developing (1) tools for analyzing brain activity; (2) a theoretical framework for expressing underlying computations and generating experimental predictions. The starting point of the project is our earlier discovery that phase structure in oscillatory local field potentials (LFP) of hippocampal areas CA1/CA3 carry location information in exquisite detail (Agarwal et al. 2014). We will release software tools that make the methods for phase decoding and extracting meaningful LFP components available to the broader community. Further, in collaboration with experimental labs we will research the mechanistic underpinnings of this discovery in hippocampus (Buzsaki NYU, Foster, UC Berkeley), and explore how similar approaches can leverage phase diversity in cortical gamma oscillations (Fries, MPI Frankfurt). The research goal is to develop analysis tools for decoding and extraction of functional components (Aims 1 and 2), applicable to a broad range of multivariate brain recordings of hippocampal and cortical activity. Further, we will develop a flexible two-level theory framework with software tools (Aim 3) to help neuroscientists, in particular experimenters, to formulate putative abstract computations underlying a brain function under study, and build a concrete mechanistic circuit model of those computations. The computational description level will leverage ideas of vector symbolic architectures, a class of connectionist models originally proposed for describing cognitive reasoning (Plate, 1995; Kanerva, 1996). Models produced by the software tool will concisely encapsulate assumptions about the computation and its implementation of a brain function and produce predictions that can be tested in a next generation of recording experiments. The proposed theory framework will be tested in building models for navigation in hippocampus and for visual processing in areas V1 and V4 in cortex.