Abstract：
Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.
摘要：
快速发展的大规模神经记录技术使研究人员能够在体内以单细胞分辨率测量数百至数千个神经元的活动。神经解码分析是一种广泛使用的工具，用于研究该复合体中表示的信息，高维神经种群活动。大多数群体解码方法都假设已准确估计了神经元之间的相关活动。在实践中,这需要大量的数据，跨观察和跨神经元。不幸的是,大多数实验从根本上受到实际变量的限制，这些变量限制了在单个刺激和/或行为条件下可以观察到神经群体的次数。因此,需要新的分析工具来研究神经群体编码，同时考虑到这些限制。这里,我们提出了一种简单且可解释的降维方法，可以可靠地计算神经解码指标，即使实验试验数量有限。我们使用模拟来说明该方法，并通过将其应用于从听觉皮层收集的单单元电生理数据，将其性能与用于降维和解码的标准方法进行比较。
