Impact Factor

Original Research ARTICLE

Front. Neuroinform., 18 March 2013 | http://dx.doi.org/10.3389/fninf.2013.00004

Accelerating compartmental modeling on a graphical processing unit

  • 1The Leslie and Susan Gonda Interdisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
  • 2The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel

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.

Keywords: CUDA, GPU, NEURON, ILP, parallel computing, compartmental modeling

Citation: Ben-Shalom R, Liberman G and Korngreen A (2013) Accelerating compartmental modeling on a graphical processing unit. Front. Neuro inform. 7:4. doi: 10.3389/fninf.2013.00004

Received: 09 January 2013; Paper pending published: 04 February 2013;
Accepted: 28 February 2013; Published online: 18 March 2013.

Edited by:

Andrew P. Davison, Centre National de la Recherche Scientifique, France

Reviewed by:

Michael Hines, Yale University, USA
Robert C. Cannon, Textensor Limited, UK

Copyright © 2013 Ben-Shalom, Liberman and Korngreen. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: Alon Korngreen, The Mina and Everard Goodman Faculty of life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel. e-mail: korngra@mail.biu.ac.il

These authors have contributed equally to this work.