메뉴 건너뛰기




Volumn 23, Issue 6, 2011, Pages 1503-1535

Vectorized algorithms for spiking neural network simulation

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER SIMULATION; LETTER; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY; SYNAPSE;

EID: 79958292145     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00123     Document Type: Letter
Times cited : (27)

References (47)
  • 3
    • 34147171012 scopus 로고    scopus 로고
    • Interoperability of neuroscience modeling software: Current status and future directions
    • Cannon, R. C, Gewaltig, M., Gleeson, P., Bhalla, U. S., Cornelis, N. H., et al. (2007). Interoperability of neuroscience modeling software: Current status and future directions. Neuroinformatics, 5(2), 127-138.
    • (2007) Neuroinformatics , vol.5 , Issue.2 , pp. 127-138
    • Cannon, R.C.1    Gewaltig, M.2    Gleeson, P.3    Bhalla, U.S.4    Cornelis, N.H.5
  • 6
    • 37749042762 scopus 로고    scopus 로고
    • Bayesian spiking neurons I: Inference
    • Deneve, S. (2008). Bayesian spiking neurons I: Inference. Neural Computation, 20(1), 91-117.
    • (2008) Neural Computation , vol.20 , Issue.1 , pp. 91-117
    • Deneve, S.1
  • 7
    • 0002236344 scopus 로고
    • An efficient method for computing synaptic conductances based on a kinetic model of receptor binding
    • 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(1), 14-18.
    • (1994) Neural Computation , vol.6 , Issue.1 , pp. 14-18
    • Destexhe, A.1    Mainen, Z.2    Sejnowski, T.3
  • 8
    • 0028490340 scopus 로고
    • Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism
    • Destexhe, A., Mainen, Z. F., & Sejnowski, T. J. (1994b). Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. Journal of Computational Neuroscience, 1(3), 195-230.
    • (1994) Journal of Computational Neuroscience , vol.1 , Issue.3 , pp. 195-230
    • Destexhe, A.1    Mainen, Z.F.2    Sejnowski, T.J.3
  • 9
    • 77953494546 scopus 로고    scopus 로고
    • Fast and exact simulation methods applied on a broad range of neuron models
    • D'Haene, M., & Schrauwen, B. (2010). Fast and exact simulation methods applied on a broad range of neuron models. Neural Computation, 22(6), 1468-1472.
    • (2010) Neural Computation , vol.22 , Issue.6 , pp. 1468-1472
    • D'haene, M.1    Schrauwen, B.2
  • 14
    • 43949092150 scopus 로고    scopus 로고
    • NEST (NEural Simulation Tool)
    • Gewaltig, O., & Diesmann, M. (2007). NEST (NEural Simulation Tool). Scholarpe-dia, 2(4), 1430.
    • (2007) Scholarpedia , vol.2 , Issue.4 , pp. 1430
    • Gewaltig, O.1    Diesmann, M.2
  • 15
    • 0034166633 scopus 로고    scopus 로고
    • Synthesis of generalized algorithms for the fast computation of synaptic conductances with Markov kinetic models in large network simulations
    • 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(4), 903-931.
    • (2000) Neural Computation , vol.12 , Issue.4 , pp. 903-931
    • Giugliano, M.1
  • 16
    • 0033566452 scopus 로고    scopus 로고
    • Fast calculation of short-term depressing synaptic conductances
    • Giugliano, M., Bove, M., & Grattarola, M. (1999). Fast calculation of short-term depressing synaptic conductances. Neural Computation, 11(6), 1413-1426.
    • (1999) Neural Computation , vol.11 , Issue.6 , pp. 1413-1426
    • Giugliano, M.1    Bove, M.2    Grattarola, M.3
  • 18
    • 84885847922 scopus 로고    scopus 로고
    • Brian: A simulator for spiking neural networks in Python
    • Goodman, D., & Brette, R. (2008). Brian: A simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2, 5.
    • (2008) Frontiers in Neuroinformatics , vol.2 , pp. 5
    • Goodman, D.1    Brette, R.2
  • 20
    • 79958294115 scopus 로고    scopus 로고
    • A general and efficient method for incorporating precise spike times in globally time-driven simulations
    • Hanuschkin, A., Kunkel, S., Helias, M., Morrison, A., & Diesmann, M. (2010). A general and efficient method for incorporating precise spike times in globally time-driven simulations. Frontiers in Neuroinformatics, 4(0), 12.
    • (2010) Frontiers in Neuroinformatics , vol.4 , Issue.0 , pp. 12
    • Hanuschkin, A.1    Kunkel, S.2    Helias, M.3    Morrison, A.4    Diesmann, M.5
  • 21
    • 0021322056 scopus 로고
    • Efficient computation of branched nerve equations
    • Hines, M. (1984). Efficient computation of branched nerve equations. International Journal of Bio-Medical Computing, 15(1), 69-76.
    • (1984) International Journal of Bio-Medical Computing , vol.15 , Issue.1 , pp. 69-76
    • Hines, M.1
  • 22
    • 0034180688 scopus 로고    scopus 로고
    • Expanding NEURON's repertoire of mechanisms with NMODL
    • Hines, M. L., & Carnevale, N. T. (2000). Expanding NEURON's repertoire of mechanisms with NMODL. Neural Computation, 12(5), 995-1007.
    • (2000) Neural Computation , vol.12 , Issue.5 , pp. 995-1007
    • Hines, M.L.1    Carnevale, N.T.2
  • 25
    • 33644898137 scopus 로고    scopus 로고
    • Polychronization: Computation with spikes
    • Izhikevich, E. M. (2006). Polychronization: Computation with spikes. Neural Computation, 18(2), 245-282.
    • (2006) Neural Computation , vol.18 , Issue.2 , pp. 245-282
    • Izhikevich, E.M.1
  • 26
    • 33750113770 scopus 로고    scopus 로고
    • Digital simulation of spiking neural networks
    • W. Maass & C. M. Bishop (Eds.), Cambridge, MA: MIT Press
    • Jahnke, A., Roth, U., & Schnauer, T. (1999). Digital simulation of spiking neural networks. In W. Maass & C. M. Bishop (Eds.), Pulsed neural networks (pp. 237-257). Cambridge, MA: MIT Press.
    • (1999) Pulsed neural networks , pp. 237-257
    • Jahnke, A.1    Roth, U.2    Schnauer, T.3
  • 27
    • 0032184981 scopus 로고    scopus 로고
    • Employing the Z-Transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations
    • Köhn, 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(7), 1639-1651.
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1639-1651
    • Köhn, J.1    Wörgötter, F.2
  • 28
    • 0036749743 scopus 로고    scopus 로고
    • Computation by ensemble synchronization in recurrent networks with synaptic depression
    • Loebel, A., & Tsodyks, M. (2002). Computation by ensemble synchronization in recurrent networks with synaptic depression. Journal of Computational Neuroscience, 13(2), 111-124.
    • (2002) Journal of Computational Neuroscience , vol.13 , Issue.2 , pp. 111-124
    • Loebel, A.1    Tsodyks, M.2
  • 29
    • 0030115144 scopus 로고    scopus 로고
    • Optimizing synaptic conductance calculation for network simulations
    • Lytton, W. W. (1996). Optimizing synaptic conductance calculation for network simulations. Neural Computation, 8(3), 501-509.
    • (1996) Neural Computation , vol.8 , Issue.3 , pp. 501-509
    • Lytton, W.W.1
  • 31
    • 40849102598 scopus 로고    scopus 로고
    • Synaptic theory of working memory
    • Mongillo, G., Barak, O, & Tsodyks, M. (2008). Synaptic theory of working memory. Science, 319(5869), 1543-1546.
    • (2008) Science , vol.319 , Issue.5869 , pp. 1543-1546
    • Mongillo, G.1    Barak, O.2    Tsodyks, M.3
  • 32
    • 34249703480 scopus 로고    scopus 로고
    • Spike-timing-dependent plasticity in balanced random networks
    • Morrison, A., Aertsen, A., & Diesmann, M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural Computation, 19(6), 1437-1467.
    • (2007) Neural Computation , vol.19 , Issue.6 , pp. 1437-1467
    • Morrison, A.1    Aertsen, A.2    Diesmann, M.3
  • 33
    • 43949102027 scopus 로고    scopus 로고
    • Phenomenological models of synaptic plasticity based on spike timing
    • Morrison, A., Diesmann, M., & Gerstner, W. (2008). Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics, 98(6), 459-478.
    • (2008) Biological Cybernetics , vol.98 , Issue.6 , pp. 459-478
    • Morrison, A.1    Diesmann, M.2    Gerstner, W.3
  • 34
    • 20844460509 scopus 로고    scopus 로고
    • Advancing the boundaries of high connectivity network simulation with distributed computing
    • 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.
    • (2005) Neural Computation , vol.17 , pp. 1776-1801
    • Morrison, A.1    Mehring, C.2    Geisel, T.3    Aertsen, A.4    Diesmann, M.5
  • 35
    • 33846013910 scopus 로고    scopus 로고
    • Exact subthreshold integration with continuous spike times in discrete-time neural network simulations
    • Morrison, A., Straube, S., Plesser, H. E., & Diesmann, M. (2007). Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Computation, 19,47-79.
    • (2007) Neural Computation , vol.19 , pp. 47-79
    • Morrison, A.1    Straube, S.2    Plesser, H.E.3    Diesmann, M.4
  • 36
    • 76449115517 scopus 로고    scopus 로고
    • Model sharing in computational neuroscience
    • Morse, T. (2007). Model sharing in computational neuroscience. Scholarpedia, 2(4), 3036.
    • (2007) Scholarpedia , vol.2 , Issue.4 , pp. 3036
    • Morse, T.1
  • 38
    • 78049262157 scopus 로고    scopus 로고
    • A threshold equation for action potential initiation
    • Platkiewicz, J., & Brette, R. (2010). A threshold equation for action potential initiation. PLoS Comput. Biol, 6(7), e1000850.
    • (2010) PLoS Comput. Biol , vol.6 , Issue.7
    • Platkiewicz, J.1    Brette, R.2
  • 39
    • 67650317451 scopus 로고    scopus 로고
    • Simplicity and efficiency of integrate-and-fire neuron models
    • Plesser, H. E., & Diesmann, M. (2009). Simplicity and efficiency of integrate-and-fire neuron models. Neural Computation, 21(2), 353-359.
    • (2009) Neural Computation , vol.21 , Issue.2 , pp. 353-359
    • Plesser, H.E.1    Diesmann, M.2
  • 42
    • 0033220632 scopus 로고    scopus 로고
    • Exact digital simulation of time-invariant linear systems with applications to neuronal modeling
    • Rotter, S., & Diesmann, M. (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biolomcal Cybernetics, 81(5-6), 381-402.
    • (1999) Biolomcal Cybernetics , vol.81 , Issue.5-6 , pp. 381-402
    • Rotter, S.1    Diesmann, M.2
  • 44
    • 44949119436 scopus 로고    scopus 로고
    • Why are computational neuroscience and systems biology so separate?
    • Schutter, E. D. (2008). Why are computational neuroscience and systems biology so separate? PLoS Comput. Biol., 4(5), e1000078.
    • (2008) PLoS Comput. Biol. , vol.4 , Issue.5
    • Schutter, E.D.1
  • 45
    • 0033860923 scopus 로고    scopus 로고
    • Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
    • Song, S., Miller, K. D., & Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci., 3,919-926.
    • (2000) Nature Neurosci. , vol.3 , pp. 919-926
    • Song, S.1    Miller, K.D.2    Abbott, L.F.3
  • 46
    • 0031018015 scopus 로고    scopus 로고
    • The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability
    • Tsodyks, M. V., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. PNAS, 94(2), 719-723.
    • (1997) PNAS , vol.94 , Issue.2 , pp. 719-723
    • Tsodyks, M.V.1    Markram, H.2
  • 47
    • 0032523457 scopus 로고    scopus 로고
    • Neural networks with dynamic synapses
    • Tsodyks, M., Pawelzik, K., & Markram, H. (1998). Neural networks with dynamic synapses. Neural Computation, 10(4), 821-835.
    • (1998) Neural Computation , vol.10 , Issue.4 , pp. 821-835
    • Tsodyks, M.1    Pawelzik, K.2    Markram, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.