Jakob H. Macke

TL;DR: It is shown that the use of the beta-binomial model makes it possible to determine accurate credible intervals even in data which exhibit substantial overdispersion, and Bayesian inference methods are used for estimating the posterior distribution of the parameters of the psychometric function.

TL;DR: A statistical method to detect, estimate, and correct for serial dependencies in behavioral data is developed and it is shown that even trained psychophysical observers suffer from strong history dependence.

TL;DR: This work argues that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a very small fraction of the local population, the most appropriate model captures shared variability by a low-dimensional latent process evolving with smooth dynamics, rather than by putative direct coupling.

TL;DR: This work shows how correlated binary spike trains can be simulated by means of a latent multivariate gaussian model, which naturally extends to correlations over time and offers an elegant way to model correlated neural spike counts with arbitrary marginal distributions.

TL;DR: Microscopic organization of neural codes reveals a key role of neural heterogeneity and how microscopic and population dynamics interact to make processing state-dependent.