MACHINE LEARNING APPROACHES FOR ELECTROPHYSIOLOGICAL CELL CLASSIFICATION

摘要 我们将利用我们在体感组织和可塑性方面的专业知识来开发新的和 基于神经活动的细胞识别自动化解决方案，以解码 神经电路用于信息处理的算法。感官的细胞外记录 大脑皮层被认为主要代表兴奋性神经元的活动，因为这些细胞 约占细胞总数的80%。然而，S1中的目标细胞记录显示 放电活动由抑制性神经元主导，兴奋性神经元可以表现出10-100个 根据大脑皮层的不同，降低放电率。此外，新的发现揭示了 不同亚型抑制性神经元之间的复杂关系使其难以关联 盲目记录本地或网络级计算的触发活动。显然，细胞类型很重要， 以及大量并行的细胞外记录，不能同时识别 在确定信息传输的原则和 编码。根据我们的初步发现，我们假设复合体是自发的 从分子识别的神经元中诱发的棘波序列将提供独特的和细胞类型 能够从活体细胞外记录中识别细胞的特定签名。在……里面 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.