Compressive-sensing Image Processing Through Network Dynamics

We present a new framework for recovering visual inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, our work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through integrate-and-fire network dynamics and understanding the transmission of stimulus information across the visual system.