Simulation of networks of spiking neurons: A review of tools and strategies

• Abbott, L. F., & Nelson, S. B. (2000). Synaptic plasticity: taming the beast. Nature Neuroscience, 3(Suppl), 1178–1283.

  CAS  PubMed  Google Scholar 

• Arnold, L. (1974). Stochastic differential equations: Theory and applications. New York: J. Wiley and Sons.

  Google Scholar 

• Azouz, R. (2005). Dynamic spatiotemporal synaptic integration in cortical neurons: neuronal gain, revisited. Journal of Neurophysiology, 94, 2785–2796.

  PubMed  Google Scholar 

• Badoual, M., Rudolph, M., Piwkowska, Z., Destexhe, A., & Bal, T. (2005). High discharge variability in neurons driven by current noise. Neurocomputing, 65, 493–498.

  Google Scholar 

• Bailey, J., & Hammerstrom, D. (1988). Why VLSI implementations of associative VLCNs require connection multiplexing. International Conference on Neural Networks (ICNN 88, IEEE) (pp. 173–180). San Diego.

• Banitt, Y., Martin, K. A. C., & Segev, I. (2005). Depressed responses of facilitatory synapses. Journal of Neurophysiology, 94, 865–870.

  PubMed  Google Scholar 

• Beeman, D. (2005). GENESIS Modeling Tutorial. Brains, Minds, and Media. 1: bmm220 (urn:nbn:de:0009-3-2206).

• Bernard, C., Ge, Y. C., Stockley, E., Willis, J. B., & Wheal, H. V. (1994). Synaptic integration of NMDA and non-NMDA receptors in large neuronal network models solved by means of differential equations. Biological Cybernetics, 70(3), 267–73.

  CAS  PubMed  Google Scholar 

• Bhalla, U., Bilitch, D., & Bower, J. M. (1992). Rallpacks: A set of benchmarks for neuronal simulators. Trends in Neurosciences, 15, 453–458.

  CAS  PubMed  Google Scholar 

• Bhalla, U. S., & Iyengar, R. (1999). Emergent properties of networks of biological signaling pathways. Science, 283, 381–387.

  CAS  PubMed  Google Scholar 

• Bhalla, U. S. (2004). Signaling in small subcellular volumes: II. Stochastic and diffusion effects on synaptic network properties. Biophysical Journal, 87, 745–753.

  CAS  PubMed  Google Scholar 

• Blake, J. L., & Goodman, P. H. (2002). Speech perception simulated in a biologically-realistic model of auditory neocortex (abstract). Journal of Investigative Medicine, 50, 193S.

  Google Scholar 

• Bower, J. M. (1995). Reverse engineering the nervous system: An in vivo, in vitro, and in computo approach to understanding the mammalian olfactory system. In: S. F. Zornetzer, J. L. Davis, & C. Lau (Eds.), An introduction to neural and electronic networks, second edn (pp. 3–28). New York: Academic Press.

  Google Scholar 

• Bower, J. M., & Beeman, D. (1998). The book of GENESIS: Exploring realistic neural models with the General Neural Simulation System, second edn. New York: Springer.

  Google Scholar 

• Brette, R. (2006). Exact simulation of integrate-and-fire models with synaptic conductances. Neural Computation, 18, 2004–2027.

  PubMed  Google Scholar 

• Brette, R. (2007). Exact simulation of integrate-and-fire models with exponential currents. Neural Computation (in press).

• Brette, R., & Gerstner, W. (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of Neurophysiolgy, 94, 3637–3642.

  Google Scholar 

• Brown, R. (1988). Calendar queues: A fast 0(1) priority queue implementation for the simulation event set problem. Journal of Communication ACM, 31(10), 1220–1227.

  Google Scholar 

• Carnevale, N. T., & Hines, M. L. (2006). The NEURON book. Cambridge: Cambridge University Press.

  Google Scholar 

• Carriero, N., & Gelernter, D. (1989). Linda in context. Communications of the ACM, 32, 444–458.

  Google Scholar 

• Claverol, E., Brown, A., & Chad, J. (2002). Discrete simulation of large aggregates of neurons. Neurocomputing, 47, 277–297.

  Google Scholar 

• Connollly, C., Marian, I., & Reilly, R. (2003). Approaches to efficient simulation with spiking neural networks. In WSPC.

• Cormen, T., Leiserson, C., Rivest, R., & Stein, C. (2001). Introduction to algorithms, second edn. Cambridge: MIT Press.

  Google Scholar 

• Crook, S., Beeman, D., Gleeson, P., & Howell, F. (2005). XML for model specification in neuroscience. Brains, Minds and Media 1: bmm228 (urn:nbn:de:0009-3-2282).

• Daley, D., & Vere-Jones, D. (1988). An introduction to the theory of point processes. New York: Springer.

  Google Scholar 

• Day, M., Carr, D. B., Ulrich, S., Ilijic, E., Tkatch, T., & Surmeier, D. J. (2005). Dendritic excitability of mouse frontal cortex pyramidal neurons is shaped by the interaction among HCN, Kir2, and k(leak) channels. Journal of Neuroscience, 25, 8776–8787.

  CAS  PubMed  Google Scholar 

• Delorme, A., & Thorpe, S. J. (2003). Spikenet: An event-driven simulation package for modelling large networks of spiking neurons. Network, 14(4), 613–627.

  Article  PubMed  Google Scholar 

• De Schutter, E., & Bower, J. M. (1994). An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice. Journal of Neurophysiology, 71, 375–400.

  PubMed  Google Scholar 

• Destexhe, A., Mainen, Z., & Sejnowski, T. (1994a). An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Computation, 6, 14–18.

  Google Scholar 

• Destexhe, A., Mainen, Z., & Sejnowski, T. (1994b). Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. Journal of Computational Neuroscience, 1, 195–230.

  CAS  Google Scholar 

• Destexhe, A., & Sejnowski, T. J. (2001). Thalamocortical assemblies. New York: Oxford University Press.

  Google Scholar 

• Diesmann, M., & Gewaltig, M.-O. (2002). NEST: An environment for neural systems simulations. In T. Plesser & V. Macho (Eds.), Forschung und wisschenschaftliches Rechnen, Beitrage zum Heinz-Billing-Preis 2001, Volume 58 of GWDG-Bericht, (pp. 43–70). Gottingen: Ges. fur Wiss. Datenverarbeitung.

  Google Scholar 

• Djurfeldt, M., Johansson, C., Ekeberg, Ö., Rehn, M., Lundqvist, M., & Lansner, A. (2005). Massively parallel simulation of brain-scale neuronal network models. Technical Report TRITA-NA-P0513. Stockholm: School of Computer Science and Communication.

• Drewes, R., Maciokas, J. B., Louis, S. J., & Goodman, P. H. (2004). An evolutionary autonomous agent with visual cortex and recurrent spiking columnar neural network. Lecture Notes in Computer Science, 3102, 257–258.

  Article  Google Scholar 

• Drewes, R. (2005). Modeling the brain with NCS and Brainlab. LINUX Journal online. http://www.linuxjournal.com/article/8038.

• Ermentrout, B. (2004). Simulating, analyzing, and animating dynamical systems: A guide to XPPAUT for researchers and students. Philadelphia: SIAM.

• Ermentrout, B., & Kopell, N. (1986). Parabolic bursting in an excitable system coupled with a slow oscillation. Siam Journal on Applied Mathematics, 46, 233–253.

  Google Scholar 

• Ferscha, A. (1996). Parallel and distributed simulation of discrete event systems. In A. Y. Zomaya (Ed.), Parallel and Distributed Computing Handbook (pp. 1003–1041). New York: McGraw-Hill.

  Google Scholar 

• Fransén, E., & Lansner, A. (1998). A model of cortical associative memory based on a horizontal network of connected columns. Network: Computation in Neural Systems, 9, 235–264.

  Google Scholar 

• Froemke, R. C., & Dan, Y. (2002). Spike-timing-dependent synaptic modification induced by natural spike trains. Nature, 416, 433–438.

  CAS  PubMed  Google Scholar 

• Fujimoto, R. M. (2000). Parallel and distributed simulation systems. New York: Wiley.

  Google Scholar 

• Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Booth, M., et al. (2001). Gnu scientific library: Reference manual. Bristol: Network Theory Limited.

  Google Scholar 

• Gara, A., Blumrich, M. A., Chen, D., Chiu, G. L.-T., Coteus, P., Giampapa, M. E., et al. (2005). Overview of the Blue Gene/L system architecture. IBM Journal of Reasearch and Development, 49, 195–212.

  Google Scholar 

• Gerstner, W., & Kistler, W. M. (2002). Mathematical formulations of hebbian learning. Biological Cybernetics, 87, 404–415.

  PubMed  Google Scholar 

• Giugliano, M. (2000). Synthesis of generalized algorithms for the fast computation of synaptic conductances with markov kinetic models in large network simulations. Neural Computation, 12, 903–931.

  CAS  PubMed  Google Scholar 

• Giugliano, M., Bove, M., & Grattarola, M. (1999). Fast calculation of short-term depressing synaptic conductances. Neural Computation, 11, 1413–1426.

  CAS  PubMed  Google Scholar 

• Goddard, N., Hucka, M., Howell, F., Cornelis, H., Shankar, K., & Beeman, D. (2001). Towards NeuroML: Model description methods for collaborative modelling in neuroscience. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356, 1209–1228.

  CAS  PubMed  Google Scholar 

• Grill, W. M., Simmons, A. M., Cooper, S. E., Miocinovic, S., Montgomery, E. B., Baker, K. B., et al. (2005). Temporal excitation properties of parenthesias evoked by thalamic microstimulation. Clinical Neurophysiology, 116, 1227–1234.

  PubMed  Google Scholar 

• Gupta, A., Wang, Y., & Markram, H. (2000). Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science, 287, 273–278.

  CAS  PubMed  Google Scholar 

• Gütig, R., Aharonov, R., Rotter, S., & Sompolinsky, H. (2003). Learning input correlations through non-linear asymmetric hebbian plasticity. Journal of Neuroscience, 23, 3697–3714.

  PubMed  Google Scholar 

• Gütig, R., & Sompolinsky, H. (2006). The tempotron: A neuron that learns spike timing-based decisions. Nature Neuroscience, 9, 420–428.

  PubMed  Google Scholar 

• Hammarlund, P., & Ekeberg, Ö. (1998). Large neural network simulations on multiple hardware platforms. Journal of Computational Neuroscience, 5, 443–459.

  CAS  PubMed  Google Scholar 

• Hansel, D., Mato, G., Meunier, C., & Neltner, L. (1998). On numerical simulations of integrate-and-fire neural networks. Neural Computation, 10, 467–483.

  CAS  PubMed  Google Scholar 

• Hereld, M., Stevens, R. L., Teller, J., & van Drongelen, W. (2005). Large neural simulations on large parallel computers. International Journal of Bioelectromagnetism, 7, 44–46.

  Google Scholar 

• Hindmarsh, A. C., Brown, P. N., Grant, K. E., Lee, S. L., Serban, R., Shumaker, D. E., et al. (2005). SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. ACM Transactions on Mathematical Software, 31, 363–396.

  Google Scholar 

• Hines, M. (1984). Efficient computation of branched nerve equations. International Journal of Bio-Medical Computing, 15, 69–76.

  CAS  PubMed  Google Scholar 

• Hines, M. (1989). A program for simulation of nerve equations with branching geometries. International Journal of Bio-Medical Computing, 24, 55–68.

  CAS  PubMed  Google Scholar 

• Hines, M., & Carnevale, N. T. (1997). The neuron simulation environment. Neural Computation, 9, 1179–1209.

  CAS  PubMed  Google Scholar 

• Hines, M. L., & Carnevale, N. T. (2000). Expanding NEURON’s repertoire of mechanisms with NMODL. Neural Computation, 12, 995–1007.

  CAS  PubMed  Google Scholar 

• Hines, M. L., & Carnevale, N. T. (2001). NEURON: A tool for neuroscientists. The Neuroscientist, 7, 123–135.

  CAS  PubMed  Google Scholar 

• Hines, M. L., & Carnevale, N. T. (2004). Discrete event simulation in the NEURON environment. Neurocomputing, 58–60, 1117–1122.

  Google Scholar 

• Hirsch, M., & Smale, S. (1974). Differential equations, dynamical systems, and linear algebra. Pure and applied mathematics. New York: Academic Press.

  Google Scholar 

• Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500–544.

  CAS  PubMed  Google Scholar 

• Honeycutt, R. L. (1992). Stochastic Runge–Kutta algorithms. I. White noise. Physical Review A, 45, 600–603.

  CAS  PubMed  Google Scholar 

• Houweling, A. R., Bazhenov, M., Timofeev, I., Steriade, M., & Sejnowski, T. J. (2005). Homeostatic synaptic plasticity can explain post-traumatic epileptogenesis in chronically isolated neocortex. Cerebral Cortex, 15, 834–845.

  PubMed  Google Scholar 

• Hugues, E., Guilleux, F., & Rochel, O. (2002). Contour detection by synchronization of integrate-and-fire neurons. Proceedings of the 2nd workshop on biologically motivated computer vision—BMCV 2002, TÃbingen, Germany. Lecture Notes in Computer Science, 2525, 60–69.

  Google Scholar 

• Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572.

  CAS  PubMed  Google Scholar 

• Jahnke, A., Roth, U., & Schoenauer, T. (1998). Digital simulation of spiking neural networks. In W. Maass & C. M. Bishop (Eds.), Pulsed neural networks. Cambridge: MIT Press.

  Google Scholar 

• Johnston, D., & Wu, S. M.-S. (1995). Foundations of Cellular Neurophysiology. Cambridge: MIT Press.

  Google Scholar 

• Kanold, P. O., & Manis, P. B. (2005). Encoding the timing of inhibitory inputs. Journal of Neurophysiology, 93, 2887–2897.

  PubMed  Google Scholar 

• Kellogg, M. M., Wills, H. R., & Goodman, P. H. (1999). Cumulative synaptic loss in aging and Alzheimer’s dementia: A biologically realistic computer model (abstract). Journal of Investigative Medicine, 47(17S).

• Kernighan, B. W., & Pike, R. (1984). Appendix 2: Hoc manual. In The UNIX programming environment (pp. 329–333). Englewood Cliffs: Prentice-Hall.

  Google Scholar 

• Kohn, J., & Wörgötter, F. (1998). Employing the Z-transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations. Neural Computation, 10, 1639–1651.

  CAS  PubMed  Google Scholar 

• Kozlov, A., Lansner, A., & Grillner, S. (2003). Burst dynamics under mixed nmda and ampa drive in the models of the lamprey spinal cpg. Neurocomputing, 52–54, 65–71.

  Google Scholar 

• Kozlov, A., Lansner, A., Grillner, S., & Kotaleski, J. H. (2007). A hemicord locomotor network of excitatory interneurons: A simulation study. Biological Cybernetics, 96, 229–243.

  CAS  PubMed  Google Scholar 

• Laing, C. R. (2006). On the application of “equation-free” modelling to neural systems. Journal of Computational Neuroscience, 20, 5–23.

  PubMed  Google Scholar 

• Lee, G., & Farhat, N. H. (2001). The double queue method: A numerical method for integrate-and-fire neuron networks. Neural Networks, 14, 921–932.

  CAS  PubMed  Google Scholar 

• Lundqvist, M., Rehn, M., Djurfeldt, M., & Lansner, A. (2006). Attractor dynamics in a modular network model of neocortex. Network: Computation in Neural Systems, 17, 253–276.

  Google Scholar 

• Lytton, W. W. (1996). Optimizing synaptic conductance calculation for network simulations. Neural Computation, 8, 501–509.

  CAS  PubMed  Google Scholar 

• Lytton, W. W. (2002). From computer to brain. New York: Springer-Verlag.

  Google Scholar 

• Lytton, W. W., & Hines, M. L. (2005). Independent variable time-step integration of individual neurons for network simulations. Neural Computation, 17, 903–921.

  PubMed  Google Scholar 

• Maass, W., Natschlager, T., & Markram, H. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14, 2531–2560.

  Google Scholar 

• Macera-Rios, J. C., Goodman, P. H., Drewes, R, & Harris, F. C. Jr (2004). Remote-neocortex control of robotic search and threat identification. Robotics and Autonomous Systems, 46, 97–110.

  Google Scholar 

• Maciokas, J. B., Goodman, P. H., Kenyon, J. L., Toledo-Rodriquez, M., & Markram, H. (2005). Accurate dynamical model of interneuronal GABAergic channel physiologies. Neurocomputing, 65, 5–14.

  Google Scholar 

• Makino, T. (2003). A discrete-event neural network simulator for general neuron models. Neural Computing and Applications, 11, 210–223.

  Google Scholar 

• Marian, I., Reilly, R., & Mackey, D. (2002). Efficient event-driven simulation of spiking neural networks. In Proceedings of the 3rd WSEAS International Conference on Neural Networks and Applications.

• Markram, H., Lubke, J., Frotscher, M., Roth, A., & Sakmann, B. (1997a). Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. Journal of Physiology, 500, 409–440.

  CAS  Google Scholar 

• Markram, H., Lubke, J., Frotscher, M., & Sakmann, B. (1997b). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213–215.

  CAS  Google Scholar 

• Markram, H., Dimitri, P., Gupta, A., & Tsodyks, M. (1998a). Potential for multiple mechanisms, phenomena and algorithms for synaptic plasticity at single synapses. Neuropharmacology, 37, 489–500.

  CAS  PubMed  Google Scholar 

• Markram, H., Wang, Y., & Tsodyks, M. (1998b). Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences of the United Stated of America, 95, 5323–5328.

  CAS  Google Scholar 

• Mattia, M., & Del Giudice, P. (2000). Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Computation, 12, 2305–2329.

  CAS  PubMed  Google Scholar 

• Markaki, M., Orphanoudakis, S., & Poirazi, P. (2005). Modelling reduced excitability in aged CA1 neurons as a calcium-dependent process. Neurocomputing, 65, 305–314.

  Google Scholar 

• Mayrhofer, R., Affenzeller, M., Prähofer, H., Hfer, G., & Fried, A. (2002). Devs simulation of spiking neural networks. In Proceedings of Cybernetics and Systems (EMCSR), (Vol. 2, pp. 573–578). Austrian Society for Cybernetic Studies.

• Migliore, M., Hines, M. L., & Shepherd, G. M. (2005). The role of distal dendritic gap junctions in synchronization of mitral cell axonal output. Journal of Computational Neuroscience, 18, 151–161.

  CAS  PubMed  Google Scholar 

• Migliore, M., Cannia, C., Lytton, W. W., Markram, H., & Hines, M. L. (2006). Parallel network simulations with NEURON. Journal of Computational Neuroscience, 21, 119–129.

  CAS  PubMed  Google Scholar 

• Moffitt, M. A., & McIntyre, C. C. (2005). Model-based analysis of cortical recording with silicon microelectrodes. Clinical Neurophysiology, 116, 2240–2250.

  PubMed  Google Scholar 

• Moore, J. W., & Stuart, A. E. (2000). Neurons in action: computer simulations with NeuroLab. Sunderland: Sinauer Associates.

  Google Scholar 

• Morrison, A., Aertsen, A., & Diesmann, M. (2007b). Spike-timing plasticity in balanced random networks. Neural Computation, 19, 47–49.

  Google Scholar 

• Morrison, A., Mehring, C., Geisel, T., Aertsen, A., & Diesmann, M. (2005). Advancing the boundaries of high connectivity network simulation with distributed computing. Neural Computation, 17, 1776–1801.

  PubMed  Google Scholar 

• Morrison, A., Straube, S., Plesser, H. E., & Diesmann, M. (2007a). Exact subthreshold integration with continuous spike times in discrete time neural network simulations. Neural Computation, 19, 1437–1467.

  Google Scholar 

• Natschläger, T., Markram, H., & Maass, W. (2003). Computer models and analysis tools for neural microcircuits. In R. Kötter (Ed.), Neuroscience databases. A practical guide (pp. 123–138). Boston: Kluwer Academic Publishers.

  Google Scholar 

• Nenadic, Z., Ghosh, B. K., & Ulinski, P. (2003). Propagating waves in visual cortex: A large scale model of turtle visual cortex. Journal of Computational Neuroscience, 14, 161–184.

  PubMed  Google Scholar 

• Olshausen, B. A., & Field, D. J. (2005). How close are we to understanding V1? Neural Computation, 17, 1665–1699.

  PubMed  Google Scholar 

• Opitz, B. A., & Goodman, P. H. (2005). In silico knockin/knockout in model neocortex suggests role of Ca-dependent K+ channels in spike-timing information (abstract). Journal of Investigative Medicine, 53, 193S.

  Google Scholar 

• Prescott, S. A., & De Koninck, Y. (2005). Integration time in a subset of spinal lamina I neurons is lengthened by sodium and calcium currents acting synergistically to prolong subthreshold depolarization. Journal of Neuroscience, 25, 4743–4754.

  CAS  PubMed  Google Scholar 

• Press, W. H., Flannery B. P., Teukolsky S. A., & Vetterling W. T. (1993). Numerical recipes in C: The art of scientific computing. Cambridge: Cambridge University Press.

  Google Scholar 

• Reutimann, J., Giugliano, M., & Fusi, S. (2003). Event-driven simulation of spiking neurons with stochastic dynamics. Neural Computation, 15, 811–830.

  PubMed  Google Scholar 

• Ripplinger, M. C., Wilson, C. J., King, J. G., Frye, J., Drewes, R., Harris, F. C., et al. (2004). Computational model of interacting brain networks (abstract). Journal of Investigative Medicine, 52, 155S.

  Google Scholar 

• Rochel, O., & Martinez, D. (2003). An event-driven framework for the simulation of networks of spiking neurons. In Proceedings of the 11th European Symposium on Artificial Neural Networks — ESANN’2003 (pp. 295–300). Bruges.

• Rochel, O., & Vieville, T. (2006). One step towards an abstract view of computation in spiking neural networks (abstract). 10th International Conference on Cognitive and Neural Systems. Boston.

• Rochel, O., & Cohen, N. (2007). Real time computation: Zooming in on population codes. Biosystems, 87, 260–266.

  PubMed  Google Scholar 

• Rotter, S., & Diesmann, M. (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biological Cybernetics, 81, 381–402.

  CAS  PubMed  Google Scholar 

• Rubin, J., Lee, D., & Sompolinsky, H. (2001). Equilibrium properties of temporally asymmetric Hebbian plasticity. Physical Review Letters, 86, 364–367.

  CAS  PubMed  Google Scholar 

• Rudolph, M., & Destexhe, A. (2006). Analytical integrate-and-fire neuron models with conductance-based dynamics for event-driven simulation strategies. Neural Computation, 18, 2146–2210.

  PubMed  Google Scholar 

• Rudolph, M., & Destexhe, A. (2007). How much can we trust neural simulation strategies? Neurocomputing, 70, 1966–1969.

  Google Scholar 

• Saghatelyan, A., Roux, P., Migliore, M., Rochefort, C., Desmaisons, D., Charneau, P., et al. (2005). Activity-dependent adjustments of the inhibitory network in the olfactory bulb following early postnatal deprivation. Neuron, 46, 103–116.

  CAS  PubMed  Google Scholar 

• Sanchez-Montanez, M. (2001). Strategies for the optimization of large scale networks of integrate and fire neurons. In J. Mira & A. Prieto (Eds.), IWANN, Volume 2084/2001 of Lecture Notes in Computer Science. New York: Springer-Verlag.

  Google Scholar 

• Sandström, M., Kotaleski, J. H., & Lansner, A. (2007). Scaling effects in the olfactory bulb. Neurocomputing, 70, 1802–1807.

  Google Scholar 

• Shelley, M. J., & Tao, L (2001). Efficient and accurate time-stepping schemes for integrate-and-fire neuronal networks. Journal of Computatonal Neuroscience, 11, 111–119.

  CAS  Google Scholar 

• Sleator, D., & Tarjan, R. (1983). Self-adjusting binary trees. In Proceedings of the 15th ACM SIGACT Symposium on Theory of Computing (pp. 235–245).

• Sloot, A., Kaandorp, J. A., Hoekstra, G., & Overeinder, B. J. (1999). Distributed simulation with cellular automata: Architecture and applications. In J. Pavelka, G. Tel, & M. Bartosek (Eds.), SOFSEM’99, LNCS (pp. 203–248). Berlin: Springer-Verlag.

  Google Scholar 

• Song, S., & Abbott, L. F. (2001). Cortical development and remapping through spike timing-dependent plasticity. Neuron, 32, 339–350.

  CAS  PubMed  Google Scholar 

• Song, S., Miller, K. D., & Abbott, L. F. (2000). Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3, 919–926.

  CAS  PubMed  Google Scholar 

• Stricanne, B., & Bower, J. M. (1998). A network model of the somatosensory system cerebellum, exploring recovery from peripheral lesions at various developmental stages in rats (abstract). Society of Neuroscience Abstracts, 24, 669.

  Google Scholar 

• Traub, R. D., & Miles, R. (1991). Neuronal Networks of the Hippocampus. Cambridge UK: Cambridge University Press.

  Google Scholar 

• Traub, R. D., Contreras, D., Cunningham, M. O., Murray, H., LeBeau, F. E. N., Roopun, A., et al. (2005). Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. Journal of Neurophysiology, 93, 2194–2232.

  PubMed  Google Scholar 

• Tsodyks, M., Pawelzik, K., & Markram, H. (1998). Neural networks with dynamic synapses. Neural Computation, 10, 821–835.

  CAS  PubMed  Google Scholar 

• Tuckwell, H. (1988). Introduction to theoretical neurobiology, volume 1: Linear cable theory and dendritic structure. Cambridge: Cambridge University Press.

  Google Scholar 

• van Emde Boas, P., Kaas, R., & Zijlstra, E. (1976). Design and implementation of an efficient priority queue. Theory of Computing Systems, 10, 99–127.

  Google Scholar 

• Vitko, I., Chen, Y. C., Arias, J. M., Shen, Y., Wu, X. R., & Perez-Reyes, E. (2005). Functional characterization and neuronal modeling of the effects of childhood absence epilepsy variants of CACNA1H, a T-type calcium channel. Journal of Neuroscience, 25, 4844–4855.

  CAS  PubMed  Google Scholar 

• Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. Journal of Neuroscience, 25, 10786–10795.

  CAS  PubMed  Google Scholar 

• Waikul, K. K., Jiang, L. J., Wilson, E. C., Harris, F. C. Jr, & Goodman, P. H. (2002). Design and implementation of a web portal for a NeoCortical Simulator. In Proceedings of the 17th International Conference on Computers and their Applications (CATA 2002) (pp. 349–353).

• Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., & Goldman-Rakic, P.S. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature Neuroscience, 9, 534–542.

  CAS  PubMed  Google Scholar 

• Watts, L. (1994). Event-driven simulation of networks of spiking neurons. Advances in neural information processing systems (pp. 927–934).

• Wiebers, J. L., Goodman, P. H., & Markram, H. (2000). Blockade of A-type potassium channels recovers memory impairment caused by synaptic loss: Implications for Alzheimer’s dementia (abstract). Journal of Investigative Medicine, 48, 283S.

  Google Scholar 

• Wills, H. R., Kellogg, M. M., & Goodman, P. H. (1999). A biologically realistic computer model of neocortical associative learning for the study of aging and dementia (abstract). Journal of Investigative Medicine, 47, 11S.

  Google Scholar 

• Wilson, M. A., & Bower, J. M. (1989). The simulation of large-scale neural networks. In C. Koch & I. Segev (Eds.), Methods in neuronal modeling: From synapses to networks (pp. 291–333). Cambridge: MIT Press.

  Google Scholar 

• Wohrer, A., Kornprobst, P., & Vieville, T. (2006). From light to spikes: A large-scale retina simulator. In Proceedings of the IJCNN 2006 (pp. 8995–9003). Vancouver, ISBN: 0-7803-9490-9.

• Wolf, J. A., Moyer, J. T., Lazarewicz, M. T., Contreras, D., Benoit-Marand, M., O’Donnell, P., et al. (2005). NMDA/AMPA ratio impacts state transitions and entrainment to oscillations. Journal of Neuroscience, 25, 9080–9095.

  CAS  PubMed  Google Scholar 

• Zeigler, B., Praehofer, H., & Kim, T. (2000). Theory of modeling and simulation, second edn. Integrating discrete event and continuous complex dynamic systems. New York: Academic Press.

  Google Scholar 

• Zeigler, B. P., & Vahie, S. (1993). DEVS formalism and methodology: Unity of conception/diversity of application. In Proceedings of the 1993 Winter Simulation Conference (pp. 573–579). Los Angeles, December 12–15.

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