Abstract��Neural networks have the power to deal with infor- mation which is very hard to process using ordinary approaches, e.g. speech recognition. A recent trend in applying neural net- works is to use biologically realistic neuron models. Specifically, neurons are considered which communicate with discrete pulses instead of continuous signals: spiking neurons. In this paper we investigate a small selection of properties which are found in biological neurons and investigate their effect on the general computational performance of spiking neural networks (SNN). Firstly, we investigated the way in which the internal dynamics of the neurons and delayed communication improve the ability to recognize temporal patterns. Secondly we explored an un- supervised adaptation rule which helps to distribute the work equally over all the neurons in the network, so that all neurons are involved in the task they are supposed to solve. It turned out that these biologically inspired features often improved the performance for the tasks investigated.