451
Views
0
CrossRef citations to date
0
Altmetric
Review

Simulating spiking neural networks on GPU

&
Pages 167-182 | Received 07 Jun 2012, Accepted 11 Sep 2012, Published online: 15 Oct 2012

References

  • Ahmadi A, Soleimani H. 2011. A GPU based simulation of multilayer spiking neural networks. In: 2011 19th Iranian conference on Electrical Engineering (ICEE), pp 1–5.
  • Bell N, Garland M. 2009. Implementing sparse matrix-vector multiplication on throughput- oriented processors. In: Proceedings of the conference on High Performance Computing Networking, Storage and Analysis, SC ’09, New York, NY, USA. pp 18:1–18:11. ACM.
  • Bernhard F, Keriven R. Spiking neurons on GPUs. Computational science ICCS 2006, V Alexandrov, G van Albada, P Sloot, J Dongarra. Springer Berlin, Heidelberg 2006; 236–243, Volume 3994 of lecture notes in computer science
  • Bhuiyan M, Pallipuram V, Smith M. 2010. Acceleration of spiking neural networks in emerging multi-core and GPU architectures. In: IEEE international symposium on Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 April, pp 1–8.
  • Bohte S, Slazynski L. Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks. Network 2012; . in press
  • Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, et al. Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience 2007; 23: 349–398
  • Bull JM, Smith LA, Pottage L, Freeman R. 2001. Benchmarking Java against C and Fortran for scientific applications. In: Proceedings of the 2001 joint ACM-ISCOPE conference on Java Grande, Palo Alto, California, United States, pp 97–105. ACM.
  • Carnevale NT, Hines ML. The NEURON book, 2. Cambridge University Press. 2006
  • Fernandez A, San Martin R, Farguell E, Pazienza G. 2008. Cellular neural networks simulation on a parallel graphics processing unit. In: 11th International workshop on Cellular Neural Networks and Their Applications, CNNA 2008. 2008 July, pp 208–212.
  • Fidjeland A, Roesch E, Shanahan M, Luk W. 2009. NeMo: A platform for neural modelling of spiking neurons using GPUs. In: 20th IEEE International conference on Application-specific Systems, Architectures and Processors, ASAP 2009., July 2009, pp 137–144.
  • Fidjeland A, Shanahan M. 2010. Accelerated simulation of spiking neural networks using GPUs. In: 2010 International joint conference on the Neural Networks (IJCNN), July, pp 1–8.
  • Garny A, Nickerson DP, Cooper J, dos Santos RW, Miller AK, McKeever S, Nielsen PMF, Hunter PJ. CellML and associated tools and techniques. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 2008; 366(1878)3017–3043, PMID: 18 579 471
  • Gerstner W, Kistler WM. Spiking neuron models, 2. Cambridge University Press. 2002
  • Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, Farinella M, Morse TM, Davison AP, Ray S, Bhalla US, et al. NeuroML: A language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol 2010; 6(6)e1000815
  • Goodman DFM. Code generation: A strategy for neural network simulators. Neuroinformatics 2010; 8(3)183–196
  • Goodman D, Brette R. Brian: A simulator for spiking neural networks in Python. Frontiers in Neuroinformatics 2008; 2: 5
  • Goodman DFM, Brette R. The Brian simulator. Frontiers in Neuroscience 2009; 3(2)192–197
  • Han B, Taha T. 2010a. Neuromorphic models on a GPGPU cluster. In: 2010 International joint conference on the Neural Networks (IJCNN), July, pp 1–8.
  • Han B, Taha TM. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors. Applied Optics 2010b; 49(10)B83–B91
  • Hines ML, Carnevale NT. Expanding NEURON’s repertoire of mechanisms with NMODL. Neural Computation 2000; 12(5)995–1007
  • Hoffmann J, El-Laithy K, Gttler F, Bogdan M. Simulating Biological-Inspired spiking neural networks with OpenCL. Artificial neural networks ICANN 2010, K Diamantaras, W Duch, L Iliadis. Springer Berlin, Heidelberg 2010; 184–187, Volume 6352 of Lecture Notes in Computer Science
  • Igarashi J, Shouno O, Fukai T, Tsujino H. Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units. Neural Networks: The Official Journal of the International Neural Network Society 2011; 24(9)950–960, PMID: 21 764 258
  • Kl¨ockner A, Pinto N, Lee Y, Catanzaro B, Ivanov P, Fasih A. 2009. PyCUDA: GPU Run-Time code generation for High-Performance computing. 0911.3456. Available at arXiv.org preprint server: http://arxiv.org/abs/0911.3456.
  • Kl¨ockner A, Pinto N, Lee Y, Catanzaro B, Ivanov P, Fasih A. PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation. Parallel Computing 2012; 38(3)157–174
  • Kootsey JM, Kohn MC, Feezor MD, Mitchell GR, Fletcher PR. SCoP: An interactive simulation control program for micro- and minicomputers. Bulletin of Mathematical Biology 1986; 48(3–4)427–441
  • Mascagni M. A parallelizing algorithm for computing solutions to arbitrarily branched cable neuron models. Journal of Neuroscience Methods 1991; 36(1)105–114
  • Mascagni MV, Sherman A. Numerical methods for neuronal modeling. Methods in neuronal modeling, Chap. 14, C. Koch, I. Segev. MIT Press, Cambridge, MA, USA 1989; 439–484
  • Miller A, Marsh J, Reeve A, Garny A, Britten R, Halstead M, Cooper J, Nickerson D, Nielsen P. An overview of the CellML API and its implementation. BMC Bioinformatics 2010; 11(1)178
  • Mutch J, Knoblich U, Poggio T. 2010. CNS: A GPU-based framework for simulating cortically-organized networks. Technical Report MIT-CSAIL-TR-2010-013 / CBCL-286, Massachusetts Institute of Technology, Cambridge, MA.
  • Nageswaran JM, Dutt N, Krichmar JL, Nicolau A, Veidenbaum A. 2009a. Efficient simulation of large-scale spiking neural networks using CUDA graphics processors. In: Proceedings of the 2009 international joint conference on Neural Networks, Atlanta, Georgia, USA, IEEE Press, pp 3201–3208.
  • Nageswaran JM, Dutt N, Krichmar JL, Nicolau A, Veidenbaum AV. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Networks 2009b; 22(56)791–800
  • Nowotny T. Flexible neuronal network simulation framework using code generation for NVidia CUDA. BMC Neuroscience 2011a; 12(Suppl 1)P239
  • Nowotny T. 2011b. GeNN. http://sourceforge.net/projects/genn/NVIDIA (2012). CUDA programming guide.
  • Owens JD, Luebke D, Govindaraju N, Harris M, Kruger J, Lefohn AE, Purcell TJ. A survey of general-purpose computation on graphics hardware. Computer Graphics Forum 2007; 26: 80–113
  • Pinto N, Cox D. 2011. GPU meta-programming: A case study in biologically-inspired machine vision. In: Wen-mei WH. editor. GPU Computing Gems Jade Edition, Volume 2 of Applications of GPU Computing. Morgan Kaufmann.
  • Raikov I, Cannon R, Clewley R, Cornelis H, Davison A, De Schutter E, Djurfeldt M, Gleeson P, Gorchetchnikov A, Plesser HE, et al. NineML: The network interchange for neuroscience modeling language. BMC Neuroscience 2011; 12(Suppl 1)P330, PMID: Null PMCID: PMC3240446
  • Richert M, Nageswaran JM, Dutt N, Krichmar JL. An efficient simulation environment for modeling large-scale cortical processing. Frontiers in Neuroinformatics 2011; 5: 19
  • Rossant C, Goodman DFM, Fontaine B, Platkiewicz J, Magnusson AK, Brette R. Fitting neuron models to spike trains. Frontiers in Neuroscience 2011; 5: 9
  • Rossant C, Goodman DFM, Platkiewicz J, Brette R. Automatic fitting of spiking neuron models to electrophysiological recordings. Frontiers in Neuroinformatics 2010; 4: 2, doi: 10.3389/neuro.11.002.2010
  • Scorcioni R. 2010. GPGPU implementation of a synaptically optimized, anatomically accurate spiking network simulator. In: Biomedical Sciences and Engineering Conference (BSEC), 2010, pp 1–3.
  • Wang M, Yan B, Hu J, Li P. 2011. Simulation of large neuronal networks with biophysically accurate models on graphics processors. In: 2011 International joint conference on the Neural Networks (IJCNN), pp 3184–3193.
  • Yudanov D, Shaaban M, Melton R, and Reznik L. 2010. GPU-based simulation of spiking neural networks with real-time performance amp; high accuracy. In: 2010 International joint conference on the Neural Networks (IJCNN), pp 1–8.
  • Zhang Y, Cohen J, Owens JD. Fast tridiagonal solvers on the GPU. SIGPLAN Not. 2010; 45(5)127–136

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.