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DynaSim: a MATLAB toolbox for neural modeling and simulation

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DynaSim: a MATLAB toolbox for neural modeling and simulation

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Sherfey, JS.; Soplata, AE.; Ardid-Ramírez, JS.; Roberts, EA.; Stanley, DA.; Pittman-Polletta, BR.; Kopell, NJ. (2018). DynaSim: a MATLAB toolbox for neural modeling and simulation. Frontiers in Neuroinformatics. 12:1-15. https://doi.org/10.3389/fninf.2018.00010

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Título: DynaSim: a MATLAB toolbox for neural modeling and simulation
Autor: Sherfey, Jason S. Soplata, Austin E. Ardid-Ramírez, Joan Salvador Roberts, Erik A. Stanley, David A. Pittman-Polletta, Benjamin R. Kopell, Nancy J.
Entidad UPV: Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres
Fecha difusión:
Resumen:
[EN] DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring ...[+]
Palabras clave: Dynamical systems , Neural models , GNU octave , Neuroscience gateway , Graphical user interface , Code generation , Code:matlab
Derechos de uso: Reconocimiento (by)
Fuente:
Frontiers in Neuroinformatics. (eissn: 1662-5196 )
DOI: 10.3389/fninf.2018.00010
Editorial:
Frontiers Media SA
Versión del editor: https://doi.org/10.3389/fninf.2018.00010
Código del Proyecto:
info:eu-repo/grantAgreement/ARO//W911NF-12-R-0012-02/US/Event-Driven Game Theory for Predicting Dynamical Systems/
info:eu-repo/grantAgreement/NSF//1042134/US/Cognitive Rhythms Collaborative: A Discovery Network/
info:eu-repo/grantAgreement/ONR//N00014-16-1-2832/US/ONR MURI: Neural circuits underlying symbolic processing in primate cortex and basal ganglia/
Agradecimientos:
This material is based upon research supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02, the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832, and the National ...[+]
Tipo: Artículo

References

Bokil, H., Andrews, P., Kulkarni, J. E., Mehta, S., & Mitra, P. P. (2010). Chronux: A platform for analyzing neural signals. Journal of Neuroscience Methods, 192(1), 146-151. doi:10.1016/j.jneumeth.2010.06.020

Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., … Destexhe, A. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398. doi:10.1007/s10827-007-0038-6

Börgers, C., & Kopell, N. (2005). Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Computation, 17(3), 557-608. doi:10.1162/0899766053019908 [+]
Bokil, H., Andrews, P., Kulkarni, J. E., Mehta, S., & Mitra, P. P. (2010). Chronux: A platform for analyzing neural signals. Journal of Neuroscience Methods, 192(1), 146-151. doi:10.1016/j.jneumeth.2010.06.020

Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., … Destexhe, A. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398. doi:10.1007/s10827-007-0038-6

Börgers, C., & Kopell, N. (2005). Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Computation, 17(3), 557-608. doi:10.1162/0899766053019908

Ching, S., Cimenser, A., Purdon, P. L., Brown, E. N., & Kopell, N. J. (2010). Thalamocortical model for a propofol-induced  -rhythm associated with loss of consciousness. Proceedings of the National Academy of Sciences, 107(52), 22665-22670. doi:10.1073/pnas.1017069108

Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. doi:10.1016/j.jneumeth.2003.10.009

Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3(S11), 1184-1191. doi:10.1038/81460

EatonJ. W. BatemanD. HaubergS. WehbringR. GNU Octave Version 4.2.0 Manual: A High-Level Interactive Language for Numerical Computations2016

Ermentrout, B. (2002). Simulating, Analyzing, and Animating Dynamical Systems. doi:10.1137/1.9780898718195

FitzHugh, R. (1955). Mathematical models of threshold phenomena in the nerve membrane. The Bulletin of Mathematical Biophysics, 17(4), 257-278. doi:10.1007/bf02477753

Gewaltig, M.-O., & Diesmann, M. (2007). NEST (NEural Simulation Tool). Scholarpedia, 2(4), 1430. doi:10.4249/scholarpedia.1430

Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., … Silver, R. A. (2010). NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Computational Biology, 6(6), e1000815. doi:10.1371/journal.pcbi.1000815

Goodman, D. (2008). Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2. doi:10.3389/neuro.11.005.2008

Goodman, D. F. M. (2009). The Brian simulator. Frontiers in Neuroscience, 3(2), 192-197. doi:10.3389/neuro.01.026.2009

Hines, M. L., & Carnevale, N. T. (1997). The NEURON Simulation Environment. Neural Computation, 9(6), 1179-1209. doi:10.1162/neco.1997.9.6.1179

Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764

Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., … Wang. (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524-531. doi:10.1093/bioinformatics/btg015

Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569-1572. doi:10.1109/tnn.2003.820440

Kopell, N., Ermentrout, G. B., Whittington, M. A., & Traub, R. D. (2000). Gamma rhythms and beta rhythms have different synchronization properties. Proceedings of the National Academy of Sciences, 97(4), 1867-1872. doi:10.1073/pnas.97.4.1867

Kramer, M. A., Roopun, A. K., Carracedo, L. M., Traub, R. D., Whittington, M. A., & Kopell, N. J. (2008). Rhythm Generation through Period Concatenation in Rat Somatosensory Cortex. PLoS Computational Biology, 4(9), e1000169. doi:10.1371/journal.pcbi.1000169

Lorenz, E. N. (1963). Deterministic Nonperiodic Flow. Journal of the Atmospheric Sciences, 20(2), 130-141. doi:10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2

Markram, H., Meier, K., Lippert, T., Grillner, S., Frackowiak, R., Dehaene, S., … Saria, A. (2011). Introducing the Human Brain Project. Procedia Computer Science, 7, 39-42. doi:10.1016/j.procs.2011.12.015

McDougal, R. A., Morse, T. M., Carnevale, T., Marenco, L., Wang, R., Migliore, M., … Hines, M. L. (2016). Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. Journal of Computational Neuroscience, 42(1), 1-10. doi:10.1007/s10827-016-0623-7

Meng, L., Kramer, M. A., Middleton, S. J., Whittington, M. A., & Eden, U. T. (2014). A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data. PLoS ONE, 9(1), e85269. doi:10.1371/journal.pone.0085269

Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35(1), 193-213. doi:10.1016/s0006-3495(81)84782-0

Rudolph, M., & Destexhe, A. (2007). How much can we trust neural simulation strategies? Neurocomputing, 70(10-12), 1966-1969. doi:10.1016/j.neucom.2006.10.138

Stimberg, M., Goodman, D. F. M., Benichoux, V., & Brette, R. (2014). Equation-oriented specification of neural models for simulations. Frontiers in Neuroinformatics, 8. doi:10.3389/fninf.2014.00006

Traub, R. D., Buhl, E. H., Gloveli, T., & Whittington, M. A. (2003). Fast Rhythmic Bursting Can Be Induced in Layer 2/3 Cortical Neurons by Enhancing Persistent Na+Conductance or by Blocking BK Channels. Journal of Neurophysiology, 89(2), 909-921. doi:10.1152/jn.00573.2002

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