The shape of neural dependence. Export

@article{citeulike:91730, abstract = {The product-moment correlation coefficient is often viewed as a natural measure of dependence. However, this equivalence applies only in the context of elliptical distributions, most commonly the multivariate gaussian, where linear correlation indeed sufficiently describes the underlying dependence structure. Should the true probability distributions deviate from those with elliptical contours, linear correlation may convey misleading information on the actual underlying dependencies. It is often the case that probability distributions other than the gaussian distribution are necessary to properly capture the stochastic nature of single neurons, which as a consequence greatly complicates the construction of a flexible model of covariance. We show how arbitrary probability densities can be coupled to allow greater flexibility in the construction of multivariate neural population models.}, address = {Departments of Psychology and Physiology and the Waisman Center, University of Wisconsin-Madison, Madison, WI 53706, USA. rjenison@wisc.edu}, author = {Jenison, R. L. and Reale, R. A.}, citeulike-article-id = {91730}, citeulike-linkout-0 = {http://dx.doi.org/10.1162/089976604322860659}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/15025825}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=15025825}, doi = {10.1162/089976604322860659}, issn = {0899-7667}, journal = {Neural computation}, keywords = {copulas, statistics}, month = {April}, number = {4}, pages = {665--672}, posted-at = {2007-07-20 06:49:03}, priority = {2}, title = {The shape of neural dependence.}, url = {http://dx.doi.org/10.1162/089976604322860659}, volume = {16}, year = {2004} }

TY - JOUR ID - citeulike:91730 L3 - citeulike-article-id:91730 N2 - The product-moment correlation coefficient is often viewed as a natural measure of dependence. However, this equivalence applies only in the context of elliptical distributions, most commonly the multivariate gaussian, where linear correlation indeed sufficiently describes the underlying dependence structure. Should the true probability distributions deviate from those with elliptical contours, linear correlation may convey misleading information on the actual underlying dependencies. It is often the case that probability distributions other than the gaussian distribution are necessary to properly capture the stochastic nature of single neurons, which as a consequence greatly complicates the construction of a flexible model of covariance. We show how arbitrary probability densities can be coupled to allow greater flexibility in the construction of multivariate neural population models. IS - 4 JF - Neural computation SN - 0899-7667 AD - Departments of Psychology and Physiology and the Waisman Center, University of Wisconsin-Madison, Madison, WI 53706, USA. rjenison@wisc.edu EP - 672 TI - The shape of neural dependence. VL - 16 SP - 665 KW - copulas KW - statistics AU - Jenison, RL AU - Reale, RA PY - 2004/04// UR - http://dx.doi.org/10.1162/089976604322860659 ER -