Large-scale Simulation of Neuronal Systems

This thesis provides conceptual, mathematical and software methods and tools to enable and facilitate the simulation of large-scale neuronal systems on supercomputers. A perspective on the role of large-scale models in neuroscience is given and a terminology for classifying models of neuronal networks is proposed. A novel formalism for the description of connectivity of neuronal network models is presented. This formalism, the connection-set algebra, can be used both to provide concise and unambiguous descriptions of connectivity in papers in computational neuroscience, and as a component of simulator scripting languages. Two different approaches to modularity when simulating systems of networks are provided in the See simulator and in the MUSIC API and library. A parallel simulation library for neuronal network models, SPLIT, is improved and extended to handle large-scale models. Using this library, a neuronal network model of layers II/III of the neocortex, based on biophysical model neurons is simulated. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. The memory capacity of two models of different network size is measured and compared to that of an artificial neural network. We conclude that the layer II/III model performs as a robust auto-associative memory. Furthermore the model is robust against perturbation of parameters, which is a hallmark of correct models of living systems.