Efficient Simulation Scheme for Spiking Neural Networks

Nearly all neuronal information processing and inter-neuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their longterm dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of "event-driven" simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we design, implement and evaluate critically an event-driven algorithm (EDLUT) that uses pre-calculated lookup tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly-connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend upon spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.