MACHINE LEARNING APPROACHES FOR ELECTROPHYSIOLOGICAL CELL CLASSIFICATION

摘要 我们将利用我们在躯体感觉组织和可塑性方面的专业知识， 基于神经活动的细胞识别的自动化解决方案，以便解码 神经回路用于信息处理的算法。感觉神经细胞外记录 皮质被认为主要代表兴奋性神经元活动，因为这些细胞 占总细胞群的约80%。然而，S1中的靶细胞记录显示， 放电活动由抑制性神经元主导，兴奋性神经元可以显示10-100 根据皮质层的不同，放电频率会降低。此外，新的发现揭示了 不同亚型的抑制性神经元之间的复杂关系使其难以联系 盲目记录的点火活动，以本地或网络级的计算。显然，细胞类型很重要， 以及大量平行的细胞外记录， 多种细胞类型的识别将在识别信息传输的原理方面受到限制， 编码.基于我们的初步发现，我们假设复杂的自发性 从分子识别的神经元诱发的尖峰序列将提供独特的细胞类型 这将使得能够从体内细胞外记录中识别细胞。在 与卡内基梅隆大学的计算机科学家合作，我们将开发机器学习 使用从体外和体内记录收集的数据进行细胞分类的算法。

ABSTRACT We will use our expertise in somatosensory organization and plasticity to develop novel and automated solutions for cell identification based upon neural activity, in order to decode the algorithms neural circuits use for information processing. Extracellular recordings in sensory cortex have been thought to primarily represent excitatory neuron activity, since these cells comprise ~80% of the total cell population. However, targeted cell recordings in S1 reveal that firing activity is dominated by inhibitory neurons, and that excitatory neurons can show 10-100 fold lower firing rates depending on cortical layer. Furthermore, new findings that reveal the complex relationship between different subtypes of inhibitory neurons make it difficult to relate blindly-recorded firing activity to local- or network-level computations. Clearly, cell-types matter, and massively parallel extracellular recordings that do not enable the simultaneous identification of multiple cell types will be limited in identifying principles for information transmission and encoding. Based on our preliminary findings, we hypothesize that the complex spontaneous and evoked spike trains from molecularly-identified neurons will provide unique and cell-type specific signatures that will enable cell identification from in vivo extracellular recordings. In collaboration with computer scientists at Carnegie Mellon, we will develop machine-learning algorithms for cell classification, using data collected from in vitro and in vivo recordings.