A neural network model with incremental Hebbian learning of afferent and lateral synaptic couplings is proposed,which simulates the activity-dependent self-organization of grating cells in upper layers of striate cortex. These cells, found in areas V1 and V2 of the visual cortex of monkeys, respond vigorously and exclusively to bar gratings of a preferred orientation and periodicity. Response behavior to varying contrast and to an increasing number of bars in the grating show threshold and saturation effects. Their location with respect to the underlying orientation map and their nonlinear response behavior are investigated. The number of emerging grating cells is controlled in the model by the range and strength of the lateral coupling structure.

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Self-organization of local cortical circuits and cortical orientation maps: a nonlinear Hebbian model of the visual cortex with adaptive lateral couplings.

A nonlinear, recurrent neural network model of the visual cortex is presented. Orientation maps emerge from adaptable afferent as well as plastic local intracortical circuits driven by random input stimuli. Lateral coupling structures self-organize into DOG profiles under the influence of pronounced emerging cortical activity blobs. The model's simplified architecture and features are modeled to largely mimik neurobiological findings.

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PubMedDate not supplied

An incremental Hebbian learning model of the primary visual cortex with lateral plasticity and real input patterns.

We present a simplified binocular neural network model of the primary visual cortex with separate ON/OFF-pathways and modifiable afferent as well as intracortical synaptic couplings. Random as well as natural image stimuli drive the weight adaptation which follows Hebbian learning rules stabilized with constant norm and constant sum constraints. The simulations consider the development of orientation and ocular dominance maps under different conditions concerning stimulus patterns and lateral couplings. With random input patterns realistic orientation maps with +/- 1/2-vortices mostly develop and plastic lateral couplings self-organize into mexican hat type structures on average. Using natural greyscale images as input patterns, realistic orientation maps develop as well and the lateral coupling profiles of the cortical neurons represent the two point correlations of the input image used.

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PubMedDate not supplied

Primary sarcoma of the inferior vena cava: review of diagnosis, treatment, and outcomes in a case series.

Leiomyosarcoma of the inferior vena cava is a rare mesenchymal tumor. The diagnostic approach, based on general guidelines of oncologic surgery, seems to be relatively routine; specific aspects of treatment, including vascular reconstruction, depend on tumor stage, grade, and location. In this report, the management of this disease in 5 patients is summarized and the literature is reviewed. A thorough diagnostic assessment includes sonography, computed tomography, angiography or duplex ultrasonography, perioperative pathohistologic examination, and appropriate differential diagnosis. Radical resection is associated with the best outcome and long-term survival. In this series, 4 of 5 patients underwent tumor resection. In 2 patients, the disease was classified as R0. Another patient had R1 status found at resection and underwent postoperative radiation after the tumor bed was marked intraoperatively. She has remained stable since treatment. One patient died of pulmonary metastases 32 months after primary R1 tumor resection. The 5th patient has been stable since diagnosis; resection was not possible because of severe accompanying diseases and because consent for surgical intervention could not be obtained from the patient. There is reasonable hope that leiomyosarcoma of the inferior vena cava can be treated successfully, even in advanced stages, with novel antineoplastic drugs and radiotherapeutic protocols. However, general treatment recommendations have not yet been compiled.

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