The Influence of U.S. Chain Restaurant Food Consumption and Obesity in China and South Korea: An Ecological Perspective of Food Consumption, Self-Efficacy in Weight Management, Willingness to Communicate About Weight/Diet, and Depression.

This study examined the impact of U.S. chain restaurant food consumption in China and South Korea from an ecological perspective. Specifically, it explored the relationships among several environmental and individual variables that have been found to affect obesity/weight management in previous research, including the prevalence/popularity of U.S. chain restaurants in these countries, frequency of U.S. chain restaurant food consumption, self-efficacy in weight management, willingness to communicate about weight/diet, self-perceptions of weight/obesity stigma, body mass index (BMI), and depression. The results indicated that willingness to communicate about weight/diet predicted increased self-efficacy in weight management. Higher BMI scores were found to predict increased weight/obesity stigma, and increased frequency of U.S. restaurant food consumption, weight/obesity stigma, and reduced self-efficacy in weight management were found to predict increased levels of depression. The theoretical and practical implications of the findings are discussed, along with limitations and directions for future research.

Fine-tuning and the stability of recurrent neural networks.

A central criticism of standard theoretical approaches to constructing stable, recurrent model networks is that the synaptic connection weights need to be finely-tuned. This criticism is severe because proposed rules for learning these weights have been shown to have various limitations to their biological plausibility. Hence it is unlikely that such rules are used to continuously fine-tune the network in vivo. We describe a learning rule that is able to tune synaptic weights in a biologically plausible manner. We demonstrate and test this rule in the context of the oculomotor integrator, showing that only known neural signals are needed to tune the weights. We demonstrate that the rule appropriately accounts for a wide variety of experimental results, and is robust under several kinds of perturbation. Furthermore, we show that the rule is able to achieve stability as good as or better than that provided by the linearly optimal weights often used in recurrent models of the integrator. Finally, we discuss how this rule can be generalized to tune a wide variety of recurrent attractor networks, such as those found in head direction and path integration systems, suggesting that it may be used to tune a wide variety of stable neural systems.