Abstract :
Insights into brain´s high-level computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical simulator, C2, incorporating several algorithmic enhancements to optimize the simulation scale and time, through: computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion; memory efficient representation of simulation state; and communication efficient message exchanges. Using phenomenological, single-compartment models of spiking neurons and synapses with spike-timing dependent plasticity, we represented a rat-scale cortical model (55 million neurons, 442 billion synapses) in STB memory of a 32, 768-processor BlueGene/L. With 1 millisecond resolution for neuronal dynamics and 1--20 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate. In summary, by combining state-of-the-art hardware with innovative algorithms and software design, we simultaneously achieved unprecedented time-to-solution on an unprecedented problem size.
