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Extracting individual neural activity recorded through splayed optical microfibers

机译:提取通过张开的光学微纤维记录的单个神经活动

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摘要

Previously introduced bundles of hundreds or thousands of microfibers have the potential to extend optical access to deep brain regions, sampling fluorescence activity throughout a three-dimensional volume. Each fiber has a small diameter ( ) and follows a path of least resistance, splaying during insertion. By superimposing the fiber sensitivity profile for each fiber, we model the interface properties for a simulated neural population. Our modeling results suggest that for small ( ) bundles of fibers, each fiber will collect fluorescence from a small number of nonoverlapping neurons near the fiber apertures. As the number of fibers increases, the bundle delivers more uniform excitation power to the region, moving to a regime where fibers collect fluorescence from more neurons and there is greater overlap between neighboring fibers. Under these conditions, it becomes feasible to apply source separation to extract individual neural contributions. In addition, we demonstrate a source separation technique particularly suited to the interface. Our modeling helps establish performance expectations for this interface and provides a framework for estimating neural contributions under a range of conditions.
机译:先前引入的成百上千的超细纤维束具有将光学访问扩展到大脑深部区域的潜力,可以在三维空间中采样荧光活动。每根光纤的直径()小,并且沿着最小的阻力路径在插入过程中会张开。通过叠加每根光纤的光纤灵敏度曲线,我们为模拟的神经种群建模了接口属性。我们的建模结果表明,对于一小束纤维,每根纤维将从纤维孔附近的少量非重叠神经元收集荧光。随着纤维数量的增加,束向该区域传递更均匀的激发力,移动到一种状态,在该状态下纤维从更多的神经元中收集荧光,相邻纤维之间的重叠更大。在这些条件下,应用源分离来提取单个神经贡献变得可行。此外,我们演示了一种特别适合于界面的源分离技术。我们的建模有助于为此接口建立性能预期,并提供一个框架来估算一系列条件下的神经贡献。

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