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Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory

A recent computational theory suggests that visual processing in the retina and the lateral geniculate nucleus (LGN) serves to recode information into an efficient form (Atick and Redlich, 1990). Information theoretic analysis showed that the representation of visual information at the level of the photoreceptors is inefficient, primarily attributable to a high degree of spatial and temporal correlation in natural scenes. It was predicted, therefore, that the retina and the LGN should recode this signal into a decorrelated form or, equivalently, into a signal with a “white” spatial and temporal power spectrum. In the present study, we tested directly the prediction that visual processing at the level of the LGN temporally whitens the natural visual input. We recorded the responses of individual neurons in the LGN of the cat to natural, time-varying images (movies) and, as a control, to white-noise stimuli. Although there is substantial temporal correlation in natural inputs (Dong and Atick, 1995b), we found that the power spectra of LGN responses were essentially white. Between 3 and 15 Hz, the power of the responses had an average variation of only ±10.3%. Thus, the signals that the LGN relays to visual cortex are temporarily decorrelated. Furthermore, the responses of X-cells to natural inputs can be well predicted from their responses to white-noise inputs. We therefore conclude that whitening of natural inputs can be explained largely by the linear filtering properties (Enroth-Cugell and Robson, 1966). Our results suggest that the early visual pathway is well adapted for efficient coding of information in the natural visual environment, in agreement with the prediction of the computational theory.