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.

The Right to Be Forgotten More Than a Pandora’s Box?

Recently, political voices have stressed the need to introduce a right to be forgotten as new human right. Individuals should have the right to make potentially damaging information disappear after a certain time has elapsed. Such new right, however, can come in conflict with the principle of free speech. Therefore, its scope needs to be evaluated in the light of appropriate data protection rules. Insofar, a more user-centered approach is to be realized. “Delete” can not be a value as such, but must be balanced within a new legal framework.