模式分离的信息几何表述与现有指标评估
An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices
AI总结：
本文提出模式分离的信息几何框架，通过双神经元系统评估现有尖峰相似性指标，发现它们对边缘放电率敏感但忽略相关性变化，为改进模式分离度量提供依据。
摘要：
模式分离是一个计算过程，通过该过程从相似的输入模式生成不相似的神经模式。我们提出了模式分离的信息几何表述，其中模式分离器被建模为流形上的一族统计分布。这样的流形将输入（即坐标）映射到生成放电模式的概率分布。当小的坐标变化导致相应分布样本之间的距离较大时，就会发生模式分离。在这种表述下，我们实现了一个双神经元系统，其概率定律形成一个三维流形，具有相互正交的坐标，分别表示神经元的边缘放电率和相关放电率。我们使用这个高度受控的系统来检查模式分离研究中常用的尖峰序列相似性指标的行为。我们发现，所有指标（除了缩放因子）对边缘放电率的相对差异敏感，但没有指标能充分捕捉由改变两个神经元之间活动相关性而产生的尖峰序列差异。也就是说，现有的模式分离指标似乎（A）对由不同神经元编码的模式敏感，但（B）对仅在相对尖峰时间上不同的模式不敏感（例如，神经元集合中的同步性）。
英文摘要：
Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modelled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e. coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a 3-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behaviour of spike train similarity indices commonly used in pattern separation research. We found that all indices (except scaling factor) were sensitive to relative differences in marginal firing rates, but no index adequately captured differences in spike trains that resulted from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons, but (B) insensitive to patterns that differ only in relative spike timing (e.g. synchrony between neurons in the ensemble).
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。
