Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus:
Experimental Test of a Computational Theory
Yang Dan
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
due 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. While the temporal power spectrum of the natural input is 1/w2-
at relatively low spatial frequencies (far from white), we found that the
power spectra of LGN response were essentially white. Between 3 and 15
Hz, the power of the responses had an average variation of only 10.3%.
Thus the output of the LGN is temporally 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 largely explained 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.
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