Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial

Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.

Want Amid Plenty: Developing and Testing a Cross-National Measure of Anomie

One of the biggest challenges facing researchers trying to empirically test structural or institutional anomie theories is the operationalization of the key concept of anomie. This challenge is heightened by the data constraints involved in cross-national research. As a result, researchers have been forced to rely on surrogate or proxy measures of anomie and indirect tests of the theories. The purpose of this study is to examine an innovative and more theoretically sound measure of anomie and to test its ability to make cross-national predictions of serious crime. Our results are supportive of the efficacy of this construct to explain cross-national variations in crime rates. Nations with the highest rates of structural anomie also have the highest predicted rates of homicide.