School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA.
Neural Comput. 2010 Jun;22(6):1445-67. doi: 10.1162/neco.2010.04-09-989.
We present an integrative formalism of mutual information expansion, the general Poisson exact breakdown, which explicitly evaluates the informational contribution of correlations in the spike counts both between and within neurons. The formalism was validated on simulated data and applied to real neurons recorded from the rat somatosensory cortex. From the general Poisson exact breakdown, a considerable number of mutual information measures introduced in the neural computation literature can be directly derived, including the exact breakdown (Pola, Thiele, Hoffmann, & Panzeri, 2003), the Poisson exact breakdown (Scaglione, Foffani, Scannella, Cerutti, & Moxon, 2008) the synergy and redundancy between neurons (Schneidman, Bialek, & Berry, 2003), and the information lost by an optimal decoder that assumes the absence of correlations between neurons (Nirenberg & Latham, 2003; Pola et al., 2003). The general Poisson exact breakdown thus offers a convenient set of building blocks for studying the role of correlations in population codes.
我们提出了互信息扩展的综合形式，即广义泊松精确分解，它明确地评估了神经元间和神经元内的尖峰计数相关性的信息贡献。该形式在模拟数据上进行了验证，并应用于从大鼠体感皮层记录的真实神经元。从广义泊松精确分解中，可以直接推导出神经计算文献中引入的许多互信息度量，包括精确分解（Pola、Thiele、Hoffmann 和 Panzeri，2003 年）、泊松精确分解（Scaglione、Foffani、Scannella、Cerutti 和 Moxon，2008 年）、神经元间的协同作用和冗余性（Schneidman、Bialek 和 Berry，2003 年）以及假设神经元间不存在相关性的最优解码器丢失的信息（Nirenberg 和 Latham，2003 年；Pola 等人，2003 年）。因此，广义泊松精确分解为研究群体编码中相关性的作用提供了一组方便的构建块。
