Dynamical modeling of multi-scale variability in neuronal competition

摘要

Variability is observed at multiple-scales in the brain and ubiquitous in perception. However, the nature of perceptual variability is an open question. We focus on variability during perceptual rivalry, a form of neuronal competition. Rivalry provides a window into neural processing since activity in many brain areas is correlated to the alternating perception rather than a constant ambiguous stimulus. It exhibits robust properties at multiple scales including conscious awareness and neuron dynamics. The prevalent theory for spiking variability is called the balanced state; whereas, the source of perceptual variability is unknown. Here we show that a single biophysical circuit model, satisfying certain mutual inhibition architectures, can explain spiking and perceptual variability during rivalry. These models adhere to a broad set of strict experimental constraints at multiple scales. As we show, the models predict how spiking and perceptual variability changes with stimulus conditions. Benjamin P Cohen, Carson C Chow, and Shashaank Vattikuti show that dynamical mutual inhibition models can explain variability during neuronal competition at two scales: neuronal spiking activity and perceptual rivalry variability. These models make predictions for how spiking and perceptual variability will change with stimulus conditions.