Unsupervised adaptive optimization of motion-sensitive systems guided by measurement uncertainty

We propose a design for adaptive optimization of sensory systems. We consider a network of sensors that measure stimulus parameters as well as the uncertainties associated with these measurements. No prior assumptions about the stimulation and measurement uncertainties are built into the system, and properties of stimulation are allowed to vary with time. We present two approaches: one is based on estimation of the local gradient of uncertainty, and the other on random adjustment of cell tuning. Either approach steers the network towards its optimal state.