A system learns to draw independently.
The artificial neural net generates self-observed patterns. A recursive system is created wich consists out of an artificial neural network, a drawing machine and a camera. During the generative process of learning the neural network draws patterns. These patterns are a visualization of the learning process of the neural network. The network influences itsself on three levels.
In the first layer a camera processes the image. A artificial neural network as a second layer learns from the input of the camera. The third layer sends the signals from the network to the drawing machine and the drawing is again observed by the camera. This results in a recursive system that learns to draw patterns on its own.
The neural network is based on the method of the Perzeptron network of Frank Rosenblatt and the Hebbsche learning rule which werde developend in the beginning research on artificial intelligence in the 1950s. These two methods still form the conceptual basis of artificial neural networks today.
Artificial neural networks will increasingly operate in our society. You will be able to efficiently evaluate decisions, evaluate data - categorize, recognize objects and learn by means of data. The project "Neuronal Signs" visualizes the basic structure of learning in neural algorithms.
The project was developed in cooperation with Christian Faubel at the KHM, Cologne.