Recent human pharmacological fMRI studies suggest that oxytocin (OT) is a centrally-acting neurotransmitter important in the development and expression of trusting relationships in men and women. OT administration in humans was shown to increase trust, acceptance of social risk, memory of faces, and inference of the emotional state of others, in part by directly inhibiting the amygdala. However, the cerebral microcircuitry underlying this mechanism is still unclear. Here, we propose a spiking integrate-and-fire neuronal model of several key interacting brain regions affected by OT neurophysiology during social trust behavior. As a social behavior scenario, we embodied the brain simulator in a behaving virtual humanoid neurorobot, which interacted with a human via a camera. At the physiological level, the amygdala tonic firing was modeled using our recurrent asynchronous irregular nonlinear (RAIN) network architecture. OT cells were modeled with triple apical dendrites characteristic of their structure in the paraventricular nucleus of the hypothalamus. Our architecture demonstrated the success of our system in learning trust by discriminating concordant from discordant movements of a human actor. This led to a cooperative versus protective behavior by the neurorobot after being challenged by a new intent.

Summary form only given. Survival prediction is important in cancer because it determines therapy, matches patients for clinical trials, and provides patient information. Is a backpropagation neural network more accurate at predicting survival, in breast cancer, than the current staging system? For over thirty years, cancer outcome prediction has been based on the pTNM staging system. There are two problems with this system: (1) it is not very accurate, and (2) its accuracy cannot be improved because predictive variables cannot be added to the model without increasing the model's complexity to the point where it is no longer useful to the clinician. The Surveillance, Epidemiology, and End Results data set, for the years 1977 - 1985, is used to compare the predictive accuracy of the current pTNM stage system to a backpropagation neural network, for five-year breast cancer survival. The c-index is the measure of accuracy. The pTNM stage system has a c-index of .69, while the backpropagation neural network, using the same three variables, has a c-index of .73 (SE for both models is less than .01). Using the same variables, the backpropagation neural network is significantly more accurate at predicting five year survival, in breast cancer, than the current pTNM stage system.>

The generalization capabilities, for learned subtle image pattern categories, of neural network and algorithmic classification techniques are described. Several neural network and algorithmic techniques have been applied to a set of feature vectors extracted from thermal infrared images which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders, particularly with soft tissue injuries such as whiplash in which there are few widely agreed upon diagnostic standards. The results of this research indicate that the backpropagation neural network produces the best classification results and provides significantly better generalization from a set of training patterns. Results of unsupervised classification of the data using clustering algorithms and the Adaptive Resonance Theory (ART2) neural network demonstrate the difficulties of learning and of generalization of patterns from such data.>

A report is presented on a comparison between neural network and algorithmic classification techniques applied to a specific thermography program: the analysis of image patterns which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders. Of particular import is the application of thermography to soft-tissue injuries in which there are few diagnostic gold standards. Likewise, neural networks have proven to be powerful pattern separators and classifiers in a variety of real-world problems. This is primarily due to their capabilities of pattern completion and nonlinear separability in feature space. Research results show that backpropagation neural networks can accurately classify up to 90% of the whiplash thermal images, while conventional algorithmic classifiers accurately classify only up to 75%. Slight differences were found in the classification accuracy of two commercially available backpropagation implementations

The authors report on the application of neurocomputing techniques to a specific thermography program: the analysis of image patterns which characterize the extent of whiplash injury. A specific sequence of preprocessing steps designed to optimize neurocomputer classification of biomedical image patterns with symmetry is described. Data acquisition methods, preprocessing steps, and experimental results are discussed. The results presented are derived from incorporating neurocomputing analysis as part of an overall image characterization process. It is demonstrated that the features do represent certain characteristics of the pathology present in the images.>