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Bell & Howell Information and Learning: Formulae omitted ...
Theoretical studies suggest that primary visual cortex (area V1) uses a sparse code to efficiently represent natural scenes. This issue was investigated by recording from V1 neurons in awake behaving macaques during both free viewing of natural scenes and conditions simulating natural vision. Stimulation of the nonclassical receptive field increases the selectivity and sparseness of individual V1 neurons, increases the sparseness of the population response distribution, and strongly decorrelates the responses of neuron pairs. These effects are due to both excitatory and suppressive modulation of the classical receptive field by the nonclassical receptive field and do not depend critically on the spatiotemporal structure of the stimuli. During natural vision, the classical and nonclassical receptive fields function together to form a sparse representation of the visual world. This sparse code may be computationally efficient for both early vision and higher visual processing.
Although area V1 has been studied for over 40 years, little is known about how V1 encodes complex natural scenes. Theoretical studies suggest that natural scenes can be efficiently represented by a sparse code based on filters that resemble neurons found in area VI (1, 2). Sparse codes lie along a continuum ranging from dense codes, where neurons respond to most stimuli, to local codes, where neurons give extremely selective responses (3). Both of these extremes are inefficient in several important respects. Dense codes are highly redundant and each neural response carries little information, whereas local codes require an implausibly large number of neurons and are computationally intractable. In contrast, neurons that are tuned to match the sparsely distributed, informative components of the natural world can produce sparse codes. Sparse codes transmit information with minimal redundancy and relatively few spikes. Consequently, they are both informationally and metabolically more efficient than dense codes (4). There have been a few studies of sparse coding in inferior temporal visual areas (5). We have addressed this issue in area V1.
Recent theoretical studies suggest that nonlinear interactions between neurons may increase coding sparseness in area V1 (2, 6). These interactions are predominantly reflected in modulation of classical receptive field (CRF) responses by the surrounding nonclassical receptive field (nCRF) (7). Previous experiments have demonstrated that nCRF stimulation...