Theories of cortical dynamics have traditionally relied on trial-averaged
neuronal responses, largely ignoring neural variability beyond its most basic
aspects. Here we developed novel nonlinear analysis techniques to utilise a
growing body of data on the variability and co-variability of cortical
responses allowing us to delineate the operating regime of cortical dynamics.
We show that in a simple yet physiologically realistic cortical circuit model,
the stochastic supralinear stabilized network, external inputs dynamically
modify the effective connectivity, thus modulating the variability of neuronal
responses in a stimulus-dependent manner. This accounts for variability
quenching following stimulus onset, a phenomenon ubiquitously observed across
multiple cortical areas, as well as more intricate interactions between the
external input and the stimulus tuning of individual neurons in determining
Fano factors and noise correlations in area MT . The results challenge a
commonly accepted interpretation of variability quenching as a hallmark of
noisy attractor dynamics.
 A. Ponce-Alvarez, A. Thiele, T. D. Albright, G. R. Stoner and Gustavo Deco, Proc. Natl. Acad. Sci. USA 110:13162-13167 (2013).