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A self-organizing neural network model for the simultaneous and cooperative development of topographic receptive fields and lateral interactions in cortical maps is presented. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The model demonstrates how patterned lateral connections develop based on correlated activity, and explains why lateral connection patterns closely follow receptive field properties such as ocular dominance. 1 Introduction The response properties of neurons in many sensory cortical areas are ordered topographically, that is, nearby neurons respond to nearby areas of the receptor surface. Such topographic maps form by the self-organization of afferent connections to the cortex, driven b...

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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DISLEX is an artificial neural network model of the mental lexicon. It was built to test computationally whether the lexicon could consist of separate feature maps for the different lexical modalities and the lexical semantics, connected with ordered pathways. In the model, the orthographic, phonological, and semantic feature maps and the associations between them are formed in an unsupervised process, based on cooccurrence of the lexical symbol and its meaning. After the model is organized, various damage to the lexical system can be simulated, resulting in dyslexic and category-specific aphasic impairments similar to those observed in human patients. 1 Introduction The human lexical system is believed to be highly modular, consisting of a central semantic component and separate symbol memories for the different input and output modalities (Caramazza 1988; McCarthy and Warrington 1990). Such an architecture is intuitively compelling since the modalities give rise to different repres...

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...