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 byoffering more flexibility and degrees of freedom for modeling computationalelements. Although this type of Neural Networks is rather new andthere exists only a vague knowledge about its features, it is clearlymore powerful than its predecessor, not only being able to simulateArtificial Neural Networks in real time but also offering new computationalelements that were not available previously. Unfortunately, the simulationof Spiking Neural Networks currently involves the use of continuoussimulation techniques which do not scale easily to large networkswith many neurons.In this diploma thesis, a new model for Spiking Neural Networks isintroduced; it allows the use of fast discrete event simulation techniquesand possibly offers enormous advantages in terms of simulation flexibilityand scalability without restricting the qualitative computationalpower. As a proof of concept, the new model has been implementedin a prototype simulation framework, written platform-independentlyin Java. This simulation framework utilizes solely discrete eventsimulation and has been successfully used to emulate typical ArtificialNeural Networks and to simulate a biologically inspired filter model.The results of the conducted example simulations are presented andpossible 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.