title: Toward unified hybrid simulation techniques for spiking neural networks.
abstract: In the field of neural network simulation techniques, the common conception is that spiking neural network simulators can be divided in two categories: time-step-based and event-driven methods. In this letter, we look at state-of-the art simulation techniques in both categories and show that a clear distinction between both methods is increasingly difficult to define. In an attempt to improve the weak points of each simulation method, ideas of the alternative method are, sometimes unknowingly, incorporated in the simulation engine. Clearly the ideal simulation method is a mix of both methods. We formulate the key properties of such an efficient and generally applicable hybrid approach.
authors.affiliation: ELIS Department, Ghent University, 9000 Ghent, Belgium michiel.dhaene@gmail.com.
authors.forename: Michiel
authors.initials: M
authors.lastname: D'Haene
authors.forename: Michiel
authors.initials: M
authors.lastname: Hermans
authors.forename: Benjamin
authors.initials: B
authors.lastname: Schrauwen
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D012984:Software; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors