Self-Organization in Artificial Neural Networks with Biologically Inspired Spike-Rate Learning

Artificial intelligence and learning is a growing field. There are many ways of making a computer program learn, in most cases one have a specific problem one wants to solve and do not really care how it is solved. This thesis have a specific problem, but the main focus is on how it is solved. One of the most exciting ways to learn is by the so called unsupervised learning methods, where programs/agents learn without any human interaction. Psychologists and Neurologists have long tried to understand how the human brain works, but due to its complexity there are still some obstacles left before we will be able to simulate the different functionalities. This thesis is an attempt to get one step closer to solving the problem of how learning happens and memories form. If we were to be able to simulate human learning in a machine there is no telling where it could end. Jørn Hokland has put forward three learning rules that may describe how learning happens. These rules will be examined and then used in an artificial neural network with the intention to control a simulated robot. Artificial neural networks (ANNs) are more or less inspired by the biological neural networks (BNNs) found in humans and animals. As we will see this thesis seeks to be one of the more biologically inspired