  Competitive neural networks are often used to model the dynamics of
perceptual bistability. Switching between percepts can occur through
fluctuations and/or a slow adaptive process. Here, we analyze switching
statistics in competitive networks with short term synaptic depression and
noise. We start by analyzing a ring model that yields spatially structured
solutions and complement this with a study of a space-free network whose
populations are coupled with mutual inhibition. Dominance times arising from
depression driven switching can be approximated using a separation of
timescales in the ring and space-free model. For purely noise-driven switching,
we use energy arguments to justify how dominance times are exponentially
related to input strength. We also show that a combination of depression and
noise generates realistic distributions of dominance times. Unimodal functions
of dominance times are more easily differentiated from one another using
Bayesian sampling, suggesting synaptic depression induced switching transfers
more information about stimuli than noise-driven switching. Finally, we analyze
a competitive network model of perceptual tristability, showing depression
generates a memory of previous percepts based on the ordering of percepts.
