title: Building functional networks of spiking model neurons.
abstract: Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.
authors.affiliation: Department of Neuroscience, Columbia University College of Physicians and Surgeons, New York, New York, USA.|Department of Physiology and Cellular Biophysics, Columbia University College of Physicians and Surgeons, New York, New York, USA.
authors.forename: L F
authors.initials: LF
authors.lastname: Abbott
authors.affiliation: Department of Neuroscience, Columbia University College of Physicians and Surgeons, New York, New York, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: DePasquale
authors.affiliation: Department of Neuroscience, Columbia University College of Physicians and Surgeons, New York, New York, USA.|Department for Neuroinformatics, Donders Institute for Brain Cognition and Behavior, Radboud University, Nijmegen, the Netherlands.
authors.forename: Raoul-Martin
authors.initials: RM
authors.lastname: Memmesheimer
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*