Accelerating compartmental modeling on a graphical processing unit

Frontiers in Neuroinformatics

Volumn 7, Issue 1 FEBRUARY 2013, 2013, Pages

  Ben Shalom, Roy ^a   Liberman, Gilad ^a   Korngreen, Alon ^a  

^a BAR ILAN UNIVERSITY   (Israel)

Author keywords

Action potentials; Compartmental models; CUDA; GPU; Instruction level parallelism; Membrane potential; Modeling; Neuron simulation; Parallel computing; Single neurons

Indexed keywords

ACTION POTENTIAL; ARTICLE; COMPARTMENT MODEL; COMPUTER GRAPHICS; COMPUTER MEMORY; COMPUTER PROGRAM; COMPUTER SIMULATION; GRAPHICAL PROCESSING UNIT; INFORMATION PROCESSING; MEMBRANE CONDUCTANCE; MEMBRANE POTENTIAL; NERVE CELL STIMULATION; NEUROPHYSIOLOGY; STATISTICAL ANALYSIS;

EID: 84887297057     PISSN: 16625196     EISSN: None     Source Type: Journal    
DOI: 10.3389/fninf.2013.00004     Document Type: Article

References (44)

1. Ben-Shalom, R., Aviv, A., Razon, B., and Korngreen, A. (2012). Optimizing ion channel models using a parallel genetic algorithm on graphical processors. Journal of Neuroscience Methods.
2. Carnevale, N.T., and Hines, M.L. (2006). The NEURON Book. Cambridge University Press.
3. Chien-Ping, L. (Year). "K3 Moore's Law in the Era of GPU Computing", in: VLSI Design Automation and Test (VLSI-DAT), 2010 International Symposium on), 5-5.
4. De Schutter, E. (1989). Computer software for development and simulation of compartmental models of neurons. Computers in biology and medicine 19, 71-81.
5. Druckmann, S., Banitt, Y., Gidon, A., Schürmann, F., Markram, H., and Segev, I. (2007). A novel multiple objective optimization framework for constraining conductancebased neuron models by experimental data. Frontiers in neuroscience 1, 7.
6. Fidjeland, A.K., Roesch, E.B., Shanahan, M.P., and Luk, W. (Year). "NeMo: A platform for neural modelling of spiking neurons using GPUs", in: Application-specific Systems, Architectures and Processors, 2009. ASAP 2009. 20th IEEE International Conference on: IEEE), 137-144.
7. Gara, A., Blumrich, M.A., Chen, D., Chiu, G.L.T., Coteus, P., Giampapa, M.E., Haring, R.A., Heidelberger, P., Hoenicke, D., and Kopcsay, G.V. (2005). Overview of the Blue Gene/L system architecture. IBM Journal of Research and Development 49, 195-212.
8. Harada, T., Koshizuka, S., and Kawaguchi, Y. (Year). "Smoothed particle hydrodynamics on GPUs", in: Computer Graphics International), 63-70.
9. Hegland, M. (1991). On the parallel solution of tridiagonal systems by wrap-around partitioning and incomplete LU factorization. Numerische Mathematik 59, 453-472.
10. Hines, M. (1984). Efficient computation of branched nerve equations. International journal of bio-medical computing 15, 69-76.
11. Hines, M. (1989). A program for simulation of nerve equations with branching geometries. International journal of bio-medical computing 24, 55-68.
12. Hines, M. (1994). The NEURON simulation program. Neural Network Simulation Environments, 147-163.
13. Hines, M.L., and Carnevale, N.T. (1997). The NEURON simulation environment. Neural Computation 9, 1179-1209.
14. Hines, M.L., and Carnevale, N.T. (2000). Expanding NEURON's repertoire of mechanisms with NMODL. Neural Computation 12, 995-1007.
15. Hines, M.L., Markram, H., and Schürmann, F. (2008). Fully implicit parallel simulation of single neurons. Journal of computational neuroscience 25, 439-448.
16. Hodgkin, A.L., and Huxley, A.F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117, 500-544.
17. Huys, Q.J.M., Ahrens, M.B., and Paninski, L. (2006). Efficient estimation of detailed single-neuron models. Journal of neurophysiology 96, 872-890.
18. Jeong, W.K., Beyer, J., Hadwiger, M., Blue, R., Law, C., Vázquez-Reina, A., Reid, R.C., Lichtman, J., and Pfister, H. (2010). Ssecrett and neurotrace: interactive visualization and analysis tools for large-scale neuroscience data sets. Computer Graphics and Applications, IEEE 30, 58-70.
19. Keren, N., Bar-Yehuda, D., and Korngreen, A. (2009a). Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones. J Physiol 587, 1413-1437.
20. Keren, N., Bar-Yehuda, D., and Korngreen, A. (2009b). Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones. The Journal of Physiology 587, 1413-1437.
21. Keren, N., Peled, N., and Korngreen, A. (2005). Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94, 3730-3742.
22. Lee, A., Yau, C., Giles, M.B., Doucet, A., and Holmes, C.C. (2010). On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Journal of Computational and Graphical Statistics 19, 769-789.
23. Manor, Y., Gonczarowski, J., and Segev, I. (1991). Propagation of action potentials along complex axonal trees. Model and implementation. Biophysical journal 60, 1411-1423.
24. Markram, H. (2006). The blue brain project. Nature Reviews Neuroscience 7, 153-160.
25. Mascagni, M. (1991). A parallelizing algorithm for computing solutions to arbitrarily branched cable neuron models. Journal of Neuroscience Methods 36, 105-114.
26. Migliore, M., Cannia, C., Lytton, W., Markram, H., and Hines, M. (2006). Parallel network simulations with NEURON. Journal of computational neuroscience 21, 119-129.
27. Nageswaran, J.M., Dutt, N., Krichmar, J.L., Nicolau, A., and Veidenbaum, A.V. (2009). A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Networks 22, 791-800.
28. Nickolls, J., Buck, I., Garland, M., and Skadron, K. (2008). Scalable parallel programming with CUDA. Queue 6, 40-53.
29. Nvidia. 2009. NVIDIA Fermi Compute Architecture Whitepaper, v. 1.1. Available: http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf.
30. Nvidia. 2011a. Nsight, v. 2.0. Available: http://developer.nvidia.com/nvidia-parallelnsight.
31. Nvidia (2011b). NVIDIA CUDA C Programming Guide Version 4.0.
32. Nvidia. 2012. NVIDIA's Next Generation CUDA Compute Architecture: Kepler TM GK110. Available: http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf.
33. Nyland, L., Harris, M., and Prins, J. (2007). Fast n-body simulation with cuda. GPU gems 3, 677-695.
34. Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., and Purcell, T.J. (Year). "A Survey of General Purpose Computation on Graphics Hardware", in: Computer graphics forum: Wiley Online Library), 80-113.
35. Plesser, H., Eppler, J., Morrison, A., Diesmann, M., and Gewaltig, M.O. (2007). Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers. Euro-Par 2007 parallel processing, 672-681.
36. Quinn, J.C., and Abarbanel, H.D.I. (2011). Data assimilation using a GPU accelerated path integral Monte Carlo approach. Journal of Computational Physics 230, 8168-8178.
37. Rall, W. (1964). Theoretical significance of dendritic trees for neuronal input-output relations. Neural theory and modeling, 73-97.
38. Ryoo, S., Rodrigues, C.I., Baghsorkhi, S.S., Stone, S.S., Kirk, D.B., and Hwu, W.-M.W. (2008a). "Optimization principles and application performance evaluation of a multithreaded GPU using CUDA", in: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming. (Salt Lake City, UT, USA: ACM).
39. Ryoo, S., Rodrigues, C.I., Baghsorkhi, S.S., Stone, S.S., Kirk, D.B., and Hwu, W.W. (Year). "Optimization principles and application performance evaluation of a multithreaded GPU using CUDA", in: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming: ACM), 73-82.
40. Stone, H.S. (1973). An efficient parallel algorithm for the solution of a tridiagonal linear system of equations. Journal of the ACM (JACM) 20, 27-38.
41. Van Geit, W., De Schutter, E., and Achard, P. (2008). Automated neuron model optimization techniques: a review. Biological cybernetics 99, 241-251.
42. Volkov, V. (Year). "Better performance at lower occupancy", in: Proceedings of the GPU Technology Conference, GTC).
43. Wang, H. (1981). A parallel method for tridiagonal equations. ACM Transactions on Mathematical Software (TOMS) 7, 170-183.
44. Wilson, M.A., Bhalla, U.S., Uhley, J.D., Bower, J.M. (1989). "GENESIS: a system for simulating neural networks., " in Advances in Neural Information Processing Systems, ed. D. Touretzky. (San Mateo, CA: Morgan Kaufmann), 485-492.