Abstract
Retinal ganglion cell outputs are less correlated across space than are natural scenes, and it has been suggested that this decorrelation is performed in the retina in order to improve efficiency and to benefit processing later in the visual system. However, sparse coding, a successful computational model of primary visual cortex, is achievable under some conditions with highly correlated inputs: most sparse coding algorithms learn the well-known sparse features of natural images and can output sparse, high-fidelity codes with or without a preceding decorrelation stage of processing. We propose that sparse coding with biologically plausible local learning rules does require decorrelated inputs, providing a possible explanation for why whitening may be necessary early in the visual system.
