神经元功能分类的信息论方法
An information theoretic approach to the functional classification of neurons
摘要：
一群神经元通常对感觉输入表现出广泛的多样性反应。功能分类的直观概念是，细胞可以聚集在一起，这样大部分的多样性就被捕获在集群的身份中，而不是集群中的个体。我们展示了如何使用信息论来精确地做出这种直觉，而不需要在刺激或反应的空间上引入度量。应用于蝾螈的视网膜神经节细胞，这种方法恢复了经典的结果，但也提供了明确的证据，超出了以前确定的子类。此外，我们发现每个神经节细胞在功能上都是独特的，即使在同一亚类中，也只需要几个棘波就可以可靠地区分细胞。
英文摘要：
A population of neurons typically exhibits a broad diversity of responses to sensory inputs. The intuitive notion of functional classification is that cells can be clustered so that most of the diversity is captured in the identity of the clusters rather than by individuals within clusters. We show how this intuition can be made precise using information theory, without any need to introduce a metric on the space of stimuli or responses. Applied to the retinal ganglion cells of the salamander, this approach recovers classical results, but also provides clear evidence for subclasses beyond those identified previously. Further, we find that each of the ganglion cells is functionally unique, and that even within the same subclass only a few spikes are needed to reliably distinguish between cells.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
