title: Accelerating compartmental modeling on a graphical processing unit.
abstract: Compartmental modeling is a widely used tool in neurophysiology but the detail and scope of such models is frequently limited by lack of computational resources. Here we implement compartmental modeling on low cost Graphical Processing Units (GPUs), which significantly increases simulation speed compared to NEURON. Testing two methods for solving the current diffusion equation system revealed which method is more useful for specific neuron morphologies. Regions of applicability were investigated using a range of simulations from a single membrane potential trace simulated in a simple fork morphology to multiple traces on multiple realistic cells. A runtime peak 150-fold faster than the CPU was achieved. This application can be used for statistical analysis and data fitting optimizations of compartmental models and may be used for simultaneously simulating large populations of neurons. Since GPUs are forging ahead and proving to be more cost-effective than CPUs, this may significantly decrease the cost of computation power and open new computational possibilities for laboratories with limited budgets.
authors.affiliation: The Leslie and Susan Gonda Interdisciplinary Brain Research Center, Bar-Ilan University Ramat Gan, Israel ; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University Ramat Gan, Israel.
authors.forename: Roy
authors.initials: R
authors.lastname: Ben-Shalom
authors.forename: Gilad
authors.initials: G
authors.lastname: Liberman
authors.forename: Alon
authors.initials: A
authors.lastname: Korngreen
keywords: CUDA; GPU; ILP; NEURON; compartmental modeling; parallel computing