No abstract

A neural model suggests how horizontal and interlaminar connections in visual cortical areas V1 and V2 develop within a laminar cortical architecture and give rise to adult visual percepts. The model suggests how mechanisms that control cortical development in the infant lead to properties of adult cortical anatomy, neurophysiology and visual perception. The model clarifies how excitatory and inhibitory connections can develop stably by maintaining a balance between excitation and inhibition. The growth of long-range excitatory horizontal connections between layer 2/3 pyramidal cells is balanced against that of short-range disynaptic interneuronal connections. The growth of excitatory on-center connections from layer 6-to-4 is balanced against that of inhibitory interneuronal off-surround connections. These balanced connections interact via intracortical and intercortical feedback to realize properties of perceptual grouping, attention and perceptual learning in the adult, and help to explain the observed variability in the number and temporal distribution of spikes emitted by cortical neurons. The model replicates cortical point spread functions and psychophysical data on the strength of real and illusory contours. The on-center, off-surround layer 6-to-4 circuit enables top-down attentional signals from area V2 to modulate, or attentionally prime, layer 4 cells in area V1 without fully activating them. This modulatory circuit also enables adult perceptual learning within cortical area V1 and V2 to proceed in a stable way.

A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a s y n thetic aperture radar (SAR) sensor. The boundary and surface processing are accomplished by an improved Boundary Contour System (BCS) and Feature Contour System (FCS), respectively, t h a t h a ve been derived from analyses of perceptual and neurobiological data. BCS/FCS processing makes structures such as motor vehicles, roads, and buildings more salient and interpretable to human observers than they are in the original imagery. Early processing by ON cells and OFF cells emb e d d e d i n s h unting centersurround network models preprocessing by lateral geniculate nucleus (LGN). Such preprocessing compensates for illumination gradients, normalizes input dynamic range, and extracts local ratio contrasts. ON cell and OFF cell outputs are combined in the BCS to de ne oriented lters that model cortical simple cells. Pooling ON and OFF outputs at simple cells overcomes complementary processing de ciencies of each c e l l t ype along concave and convex contours, and enhances simple cell sensitivity to image edges. Oriented lter outputs are recti ed and outputs sensitive to opposite contrast polarities are pooled to de ne complex cells. The complex cells output to stages of shortrange spatial competition (or endstopping) and orientational competition among hypercomplex cells. Hypercomplex cells activate long range cooperative bipole cells that begin to group image boundaries. Nonlinear feedback b e t ween bipole cells and hypercomplex cells segments image regions by cooperatively completing and regularizing the most favored boundaries while suppressing image noise and weaker boundary groupings. Boundary segmentation is performed by three copies of the BCS at small, medium, and large lter scales, whose subsequent i n teraction distances covary with the size of the lter. Filling-in of multiple surface representations occurs within the FCS at each scale via a boundary-gated di usion process. Di usion is activated by the normalized LGN ON and OFF outputs within ON and OFF lling-in domains. Di usion is restricted to the regions de ned by gating signals from the corresponding BCS boundary segmentation. The lled-in opponent O N and OFF signals are subtracted to form double opponent surface representations. These surface representations are shown by a n y of three methods to be sensitive to both image ratio contrasts and background luminance. The three scales of surface representation are then added to yield a nal multiple-scale output. The BCS and FCS are shown to perform favorably in comparison to several other techniques for speckle removal.

A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well as abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is bench marked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.