Art - Rao (G)

nature neuroscience • volume 2 no 1 • january 1999 79 Neurons that respond optimally to line segments of a particular length were first reported in early studies of the cat and monkey visual cortex1,2. These neurons, which are especially abundant in cortical layers 2 and 3, have the curious property of endstopping (or end-inhibition): a vigorous response to an optimally oriented line segment is reduced or eliminated when the same stimulus extends beyond the neuron’s classical receptive field (RF). Such ‘extra-classical’ RF effects occur in several visual cortical areas, including V1 (area 17; refs 2, 3), V2 (area 18; refs 1, 4), V4 (ref. 5) and MT6. In most of these cases, neural responses are suppressed when stimulus properties at the center, such as orientation, velocity or direction of motion, match those in the surrounding extra-classical RF. Why should a neuron that responds to a stimulus stop responding when the same stimulus extends beyond the classical RF? Some studies have postulated a role for ‘hypercomplex’ endstopped neurons in the detection of visual curvature1,7 . Others have suggested a role for these cells in detecting corners and line terminations8, occlusion9, perceptual grouping10 and illusory contours11. However, a straightforward extension of these arguments to extra-classical RF effects in different cortical areas has been difficult. We have previously shown that a model12 based on the principle of Kalman filtering can account for certain visual cortical responses in a monkey freely viewing natural images13. It was conjectured that a similar model might also account for endstopping and other extra-classical effects. Here we show simulations suggesting that extra-classical RF effects may result directly from predictive coding of natural images. The approach postulates that neural networks learn the statistical regularities of the natural world, signaling deviations from such regularities to higher processing centers. This reduces redundancy by removing the predictable, and hence redundant, components of the input signal. Roots of this idea can be found in early information-theoretic approaches to sensory processing14–16. More recently, it has been used to explain the spatiotemporal response properties of cells in the retina17–19 and lateral geniculate nucleus (LGN)20,21. Because neighboring pixel intensities in natural images tend to be correlated, values near the image center can often be predicted from surrounding values. Thus, the raw image-intensity value at each pixel can be replaced by the difference between a center pixel value and its spatial prediction from a linear weighted sum of the surrounding values. This decorrelates (or whitens) the inputs17,19 and reduces output redundancy, providing a functional explanation for center–surround receptive fields in the retina and LGN. The values of a given pixel also tend to correlate over time. A retinal/LGN cell’s phasic response can thus be interpreted as the difference between the actual input and its temporal prediction based on a linear weighted sum of past input values19–21. Similarly, the responses of retinal photoreceptors sensitive to different wavelengths are often correlated because their spectral sensitivities overlap. Thus, the L-cone (long-wavelength or ‘red’ receptor) response may predict the M-cone (medium-wavelength or ‘green’ receptor) response, and the Land M-cone responses together may predict the S-cone (short-wavelength or ‘blue’ receptor) response. Thus, the color-opponent (red – green) and blue – (red + green) channels in the retina might reflect predictive coding in the chromatic domain similar to that of the spatial and temporal domains18. Using a hierarchical model of predictive coding, we show that visual cortical neurons with extra-classical RF properties can be interpreted as residual error detectors, signaling the difference between an input signal and its statistical prediction based on an efficient internal model of natural images. articles