An Ultra High-Density Virtual Array with Nonlinear Processing of Multimodal Neural Recordings

一种具有多模态非线性处理的超高密度虚拟阵列 神经记录 神经科学的一个主要目标是记录一个区域内所有神经元的活动。 一个完整的大脑，了解神经活动和行为之间的关系。 然而，以目前的技术，直接和同时地进行通信是不可行的。 三维脑区的每个神经元。在这里，我们提出一个小说 方法，将创新的信号处理方法与光电 记录技术，以三维方式“虚拟”记录所有神经元 音量.如果成功，这种方法将使我们能够大幅增加 记录的神经元，而不需要直接光学或电访问每个神经元。 拟议的虚拟阵列技术有可能大幅提高 在一个完整的大脑中同时记录的神经元的数量相对不- 侵入性地常见的方法包括高密度电生理探针， 这是高度侵入性的，并且在记录密度和快速扫描方面也受到限制 具有有限时间分辨率的光学技术。作为替代方法，我们 建议制定一个框架，以计算方式增加记录的数量 神经元从同步的电生理学和成像记录数据。的 计算框架将从一个数据集开发，其中微观， 同时记录皮层电图（µECoG）， 用双光子钙成像在多个皮层记录下层神经元 深度我们将通过求解适当的 涉及µECoG记录和钙的前向模型的优化问题 信号.这个优化问题将使用交替凸算法来解决。

An Ultra High-Density Virtual Array with Nonlinear Processing of Multimodal Neural Recordings A major goal of neuroscience is to record the activity of all neurons in an area of an intact brain and understand the relationship between neural activity and behavior. However, with current technologies, it is not feasible to have a direct and simultaneous access to every neuron in a three-dimensional brain area. Here we propose a novel approach, combining an innovative signal processing method with optical and electrical recording technologies to `virtually' record from all neurons in a three dimensional volume. If successful, this approach will allow us to substantially increase the number of recorded neurons without the need for direct optical or electrical access to each neuron. The proposed Virtual Array technology has the potential to dramatically increase the number of simultaneously recorded neurons in an intact brain relatively non- invasively. The common approaches include high-density electrophysiological probes, which are highly invasive and also limited in the density of recording, and fast-scanning optical techniques that have limited temporal resolution. As an alternative approach, we propose to develop a framework to computationally increase the number of recorded neurons out of recording data from simultaneous electrophysiology and imaging. The computational framework will be developed from a dataset in which micro- electrocorticogram (µECoG) are recorded simultaneously while the activities of the underlying neurons is recorded with two-photon calcium imaging at multiple cortical depths. We will virtually reconstruct this single-cell activity by solving appropriate optimization problem involving forward models for µECoG recordings and calcium signals. This optimization problem will be solved using alternating convex algorithms.