NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs

The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur exponential increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of graphics processing unit (GPU) to accelerate neuronal simulation. NeuroGPU can simulate most of biologically detailed models 800x faster than traditional simulators when using multiple GPU cores, and even 10-200 times faster when implemented on relatively inexpensive GPU systems. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. Thus, NeuroGPU enables the rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.