Circuit Models of Low-Dimensional Shared Variability in Cortical Networks.

Summary Trial-to-trial variability is a reflection of the circuitry and cellular physiology that make up a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional. Previous model cortical networks cannot explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, networks of model spiking neurons internally generate low-dimensional shared variability that captures population activity recorded in vivo. Shifting spatial attention into the receptive field of visual neurons has been shown to differentially modulate shared variability within and between brain areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation. Our work provides a critical link between observed cortical circuit structure and realistic shared neuronal variability and its modulation. Highlights • Low-dimensional shared variability can be generated in spatial network models • Synaptic spatial and temporal scales determine the dimensions of shared variability • Depolarizing inhibitory neurons suppresses the population-wide fluctuations • Modeling the attentional modulation of variability within and between brain areas Population-wide fluctuations of neural population activity are widely observed in cortical recordings. Huang et al. show that turbulent dynamics in spatially ordered recurrent networks give rise to low-dimensional shared variability, which can be suppressed by depolarizing inhibitory neurons. [ABSTRACT FROM AUTHOR]