Equation-oriented specification of neural models for simulations

Frontiers in Neuroinformatics

Volumn 8, Issue FEB, 2014, Pages

  Stimberg, Marcel ^a,b   Goodman, Dan F M ^c,d   Benichoux, Victor ^a,b   Brette, Romain ^a,b  

^a UNIVERSITÉ PARIS DESCARTES   (France)

^b ECOLE NORMALE SUPÉRIEURE   (France)

^c MASSACHUSETTS EYE AND EAR INFIRMARY   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Computational neuroscience; Neuroscience; Python; Simulation; Software

Indexed keywords

N METHYL DEXTRO ASPARTIC ACID RECEPTOR;

ACTION POTENTIAL; ARTICLE; COMPUTER PROGRAM; MEMBRANE POTENTIAL; MODEL; NERVE CELL NETWORK; NEURAL MODEL; NEUROSCIENCE; POSTSYNAPTIC POTENTIAL SUMMATION; POTASSIUM CURRENT; REFRACTORY PERIOD; SIMULATION; SODIUM CURRENT;

EID: 84894081828     PISSN: 16625196     EISSN: None     Source Type: Journal    
DOI: 10.3389/fninf.2014.00006     Document Type: Article

References (26)

1. Brette, R. (2006). Exact simulation of integrate-and-fire models with synaptic conductances. Neural Comput. 18, 2004-2027. doi: 10.1162/neco.2006.18.8.2004
2. Brette, R. (2012). On the design of script languages for neural simulation. Netw. Comp. Neural 23, 150-156. doi: 10.3109/0954898X.2012.716902
3. Brette, R., and Goodman, D. F. M. (2011). Vectorized algorithms for spiking neural network simulation. Neural Comput. 23, 1503-1535. doi: 10.1162/NECO_a_00123
4. Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349-398. doi: 10.1007/s10827-007-0038-6
5. Carnevale, N. T., and Hines, M. L. (2006). The NEURON Book. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511541612
6. Crook, S. M., Bednar, J. A., Berger, S., Cannon, R., Davison, A. P., Djurfeldt, M., et al. (2012). Creating, documenting and sharing network models. Netw. Comp. Neural 23, 131-149. doi: 10.3109/0954898X.2012.722743
7. Davison, A. (2012). Automated capture of experiment context for easier reproducibility in computational research. Comput. Sci. Eng. 14, 48-56. doi: 10.1109/MCSE.2012.41
8. Destexhe, A., Mainen, Z. F., and Sejnowski, T. J. (1994). Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. J. Comput. Neurosci. 1, 195-230. doi: 10.1007/BF00961734
9. Djurfeldt, M. (2012). The connection-set algebra-a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinform 10, 287-304. doi: 10.1007/s12021-012-9146-1
10. Gewaltig, M.-O., and Diesmann, M. (2007). NEST (NEural simulation tool). Scholarpedia 2:1430. doi: 10.4249/scholarpedia.1430
11. Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., et al. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput. Biol. 6:e1000815. doi: 10.1371/journal.pcbi.1000815
12. Goodman, D., and Brette, R. (2008). Brian: a simulator for spiking neural networks in Python. Front. Neuroinform. 2:5. doi: 10.3389/neuro.11.005.2008
13. Goodman, D. F. M. (2010). Code generation: a strategy for neural network simulators. Neuroinform 8, 183-196. doi: 10.1007/s12021-010-9082-x
14. Goodman, D. F. M., and Brette, R. (2009). The Brian simulator. Front. Neurosci. 3, 192-197. doi: 10.3389/neuro.01.026.2009
15. Hines, M. L., and Carnevale, N. T. (2000). Expanding NEURON's repertoire of mechanisms with NMODL. Neural Comput. 12, 995-1007. doi: 10.1162/089976600300015475
16. Hirsch, M. W., and Smale, S. (1974). Differential Equations, Dynamical Systems, and Linear Algebra. New York, NY: Academic Press.
17. Joyner, D., Čertík, O., Meurer, A., and Granger, B. E. (2012). Open source computer algebra systems: SymPy. ACM Commun. Comput. Algebra. 45, 225-234. doi: 10.1145/2110170.2110185
18. Morrison, A., Aertsen, A., and Diesmann, M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural Comput. 19, 1437-1467. doi: 10.1162/neco.2007.19.6.1437
19. Nordlie, E., Gewaltig, M.-O., and Plesser, H. E. (2009). Towards reproducible descriptions of neuronal network models. PLoS Comput. Biol. 5:e1000456. doi: 10.1371/journal.pcbi.1000456
20. Oliphant, T. E. (2007). Python for scientific computing. Comput. Sci. Eng. 9, 10-20. doi: 10.1109/MCSE.2007.58
21. Pérez, F., and Granger, B. E. (2007). IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9, 21-29. doi: 10.1109/MCSE.2007.53
22. Raikov, I., Cannon, R., Clewley, R., Cornelis, H., Davison, A., De Schutter, E., et al. (2011). NineML: the network interchange for neuroscience modeling language. BMC Neurosci. 12(Suppl. 1):P330. doi: 10.1186/1471-2202-12-S1-P330
23. Rotter, S., and Diesmann, M. (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biol. Cybern. 81, 381-402. doi: 10.1007/s004220050570
24. Song, S., and Abbott, L. F. (2001). Cortical development and remapping through spike timing-dependent plasticity. Neuron 32, 339-350. doi: 10.1016/S0896-6273(01)00451-2
25. Song, S., Miller, K. D., and Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919-926. doi: 10.1038/78829
26. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955-968. doi: 10.1016/S0896-6273(02)01092-9