The RF-SLISSOM model integrates two separate lines of research on computational modeling of the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures such as orientation columns and patterned lateral connections can simultaneously self-organize through input-driven Hebbian adaptation. Spiking neurons with leaky integrator synapses have been used to model image segmentation and binding by synchronization and desynchronization of neuronal group activity. Although these approaches differ in how they model the neuron and what they explain, they share the same overall layout of a laterally connected two-dimensional network. This paper shows how both self-organization and segmentation can be achieved in such an integrated network, thus presenting a unified model of development and functional dynamics in the primary visual cortex. 1 Introduction Several models of the visual cortex that take into account lateral interactions between neurons hav...

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Based on a Hebbian adaptation process, the afferent and lateral connections in the RF-LISSOM model organize simultaneously and cooperatively, and form structures such as those observed in the primary visual cortex. The neurons in the model develop local receptive fields that are organized into orientation, ocular dominance, and size selectivity columns. At the same time, patterned lateral connections form between neurons that follow the receptive field organization. This structure is in a continuously-adapting dynamic equilibrium with the external and intrinsic input, and can account for reorganization of the adult cortex following retinal and cortical lesions. The same learning processes may be responsible for a number of low-level functional phenomena such as tilt aftereffects, and combined with the leaky integrator model of the spiking neuron, for segmentation and binding. The model can also be used to verify quantitatively the hypothesis that the visual cortex forms a sparse, redun...

Cells in the visual cortex are selective not only to ocular dominance and orientation of the input, but also to its size and spatial frequency. The simulations reported in this paper show how size selectivity could develop through Hebbian self-organization, and how receptive fields of different sizes could organize into columns like those for orientation and ocular dominance. The lateral connections in the network self-organize cooperatively and simultaneously with the receptive field sizes, and produce patterns of lateral connectivity that closely follow the receptive field organization. Together with our previous work on ocular dominance and orientation selectivity, these results suggest that a single Hebbian self-organizing process can give rise to all the major receptive field properties in the visual cortex, and also to structured patterns of lateral interactions, some of which have been verified experimentally and others predicted by the model. The model also suggests a functiona...

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A self-organizing model of spiking neurons with dynamic thresholds and lateral excitatory and inhibitory connections is presented and tested in the image segmentation task. The model integrates two previously separate lines of research in modeling the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures and lateral connections could self-organize through inputdriven Hebbian adaptation. Spiking neurons with leaky integrator synapses have been used to model image segmentation and binding by synchronization and desynchronization of neuronal activity. Although these approaches differ in how they model the neuron, they have the same overall layout of a laterally connected two-dimensional network. This paper shows how both self-organization and segmentation can be achieved in such a network, thus presenting a unified model of development and functional dynamics in the primary visual cortex. 1

Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input. Recently it has been proposed that internally generated input patterns, such as those found in the developing retina and in PGO waves during REM sleep, may have the same effect. Internal pattern generators would constitute an efficient way to specify, develop, and maintain functionally appropriate perceptual organization. They may help express complex structures from minimal genetic information, and retain this genetic structure within a highly plastic system. Simulations with the RF-LISSOM orientation map model indicate that such preorganization is possible, providing a computational framework for examining how genetic influences interact with visual experience. INTRODUCTION Many self-organizing computational models of cortical development have been proposed in recent years 1,2 . The most common type of such models shows that simple activitydependent learning p...

Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input, and are an effective way to model the development of face recognition abilities. However, environment-driven self-organization alone cannot account for the fact that newborn human infants will preferentially attend to face-like stimuli even immediately after birth. Recently it has been proposed that internally generated input patterns, such as those found in the developing retina and in PGO waves during REM sleep, may have the same effect on self-organization as does the external environment. Internal pattern generators constitute an efficient way to specify, develop, and maintain functionally appropriate perceptual organization. They may help express complex structures from minimal genetic information, and retain this genetic structure within a highly plastic system. Simulations with the CRF-LISSOM model show that such preorganization can account fo...

Visual areas in primates are known to have reciprocal connections. While the feedforward bottom-up processing of visual information has been studied extensively for decades, little is known about the role of the feedback connections. Existing feedback models usually employ hand-coded connections, and do not address how these connections develop. The model described in this paper shows how feedforward and feedback connections between cortical areas V1 and V2 can be learned through self-organization simultaneously. Computational experiments show that both areas can form hierarchical representations of the input with reciprocal connections that link relevant cells in the two areas. Key words: feedback connections, self-organization, visual cortex 1

Newborn humans preferentially orient to face-like patterns at birth, but months of experience with faces is required for full face processing abilities to develop. Several models have been proposed for how the interaction of genetic and evironmental influences can explain this data. These models generally assume that the brain areas responsible for newborn orienting responses are not capable of learning and are physically separate from those that later learn from real faces. However, it has been difficult to reconcile these models with recent discoveries of face learning in newborns and young infants. We propose a general mechanism by which genetically specified and environmentdriven preferences can coexist in the same visual areas. In particular, newborn face orienting may be the result of prenatal exposure of a learning system to internally generated input patterns, such as those found in PGO waves during REM sleep. Simulating this process with the HLISSOM biological model of the visual system, we demonstrate that the combination of learning and internal patterns is an efficient way to specify and develop circuitry for face perception. This prenatal learning can account for the newborn preferences for schematic and photographic images of faces, providing a computational explanation for how genetic influences interact with experience to construct a complex adaptive system.