A New Approach to a Fast Simulation of Spiking Neural Networks


Spiking Neural Networks are considered as a new computation paradigm, representing the next generation of Artificial Neural Networks by offering more flexibility and degrees of freedom for modeling computational elements. Although this type of Neural Networks is rather new and there exists only a vague knowledge about its features, it is clearly more powerful than its predecessor, not only being able to simulate Artificial Neural Networks in real time but also offering new computational elements that were not available previously. Unfortunately, the simulation of Spiking Neural Networks currently involves the use of continuous simulation techniques which do not scale easily to large networks with many neurons. In this diploma thesis, a new model for Spiking Neural Networks is introduced; it allows the use of fast discrete event simulation techniques and possibly offers enormous advantages in terms of simulation flexibility and scalability without restricting the qualitative computational power. As a proof of concept, the new model has been implemented in a prototype simulation framework, written platform-independently in Java. This simulation framework utilizes solely discrete event simulation and has been successfully used to emulate typical Artificial Neural Networks and to simulate a biologically inspired filter model. The results of the conducted example simulations are presented and possible directions for future research are given. Additionally, a few advanced techniques regarding the use of discrete event simulation, which offers some new opportunities, are shortly discussed.