BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

671 related articles for article (PubMed ID: 19615853)

  • 1. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.
    Nageswaran JM; Dutt N; Krichmar JL; Nicolau A; Veidenbaum AV
    Neural Netw; 2009; 22(5-6):791-800. PubMed ID: 19615853
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics.
    Ros E; Carrillo R; Ortigosa EM; Barbour B; Agís R
    Neural Comput; 2006 Dec; 18(12):2959-93. PubMed ID: 17052155
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity.
    Zou Q; Bornat Y; Saïghi S; Tomas J; Renaud S; Destexhe A
    Network; 2006 Sep; 17(3):211-33. PubMed ID: 17162612
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons.
    Delorme A; Thorpe SJ
    Network; 2003 Nov; 14(4):613-27. PubMed ID: 14653495
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Programmable logic construction kits for hyper-real-time neuronal modeling.
    Guerrero-Rivera R; Morrison A; Diesmann M; Pearce TC
    Neural Comput; 2006 Nov; 18(11):2651-79. PubMed ID: 16999574
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity.
    Florian RV
    Neural Comput; 2007 Jun; 19(6):1468-502. PubMed ID: 17444757
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Spike-timing-dependent plasticity in balanced random networks.
    Morrison A; Aertsen A; Diesmann M
    Neural Comput; 2007 Jun; 19(6):1437-67. PubMed ID: 17444756
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Modeling neuronal assemblies: theory and implementation.
    Eggert J; van Hemmen JL
    Neural Comput; 2001 Sep; 13(9):1923-74. PubMed ID: 11516352
    [TBL] [Abstract][Full Text] [Related]  

  • 9. SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.
    Sripad A; Sanchez G; Zapata M; Pirrone V; Dorta T; Cambria S; Marti A; Krishnamourthy K; Madrenas J
    Neural Netw; 2018 Jan; 97():28-45. PubMed ID: 29054036
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size.
    Haskell E; Nykamp DQ; Tranchina D
    Network; 2001 May; 12(2):141-74. PubMed ID: 11405420
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Advancing the boundaries of high-connectivity network simulation with distributed computing.
    Morrison A; Mehring C; Geisel T; Aertsen AD; Diesmann M
    Neural Comput; 2005 Aug; 17(8):1776-801. PubMed ID: 15969917
    [TBL] [Abstract][Full Text] [Related]  

  • 12. NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data.
    Kasabov NK
    Neural Netw; 2014 Apr; 52():62-76. PubMed ID: 24508754
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.
    Morrison A; Straube S; Plesser HE; Diesmann M
    Neural Comput; 2007 Jan; 19(1):47-79. PubMed ID: 17134317
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Dynamic parallelism for synaptic updating in GPU-accelerated spiking neural network simulations.
    Kasap B; van Opstal AJ
    Neurocomputing (Amst); 2018 May; 302():55-65. PubMed ID: 30245550
    [TBL] [Abstract][Full Text] [Related]  

  • 15. HRLSim: a high performance spiking neural network simulator for GPGPU clusters.
    Minkovich K; Thibeault CM; O'Brien MJ; Nogin A; Cho Y; Srinivasa N
    IEEE Trans Neural Netw Learn Syst; 2014 Feb; 25(2):316-31. PubMed ID: 24807031
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Self-organizing dual coding based on spike-time-dependent plasticity.
    Masuda N; Aihara K
    Neural Comput; 2004 Mar; 16(3):627-63. PubMed ID: 15006094
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Analytical integrate-and-fire neuron models with conductance-based dynamics for event-driven simulation strategies.
    Rudolph M; Destexhe A
    Neural Comput; 2006 Sep; 18(9):2146-210. PubMed ID: 16846390
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Simulations of the behavior of synaptically driven neurons via time-invariant circuit models.
    Storace M; Bove M; Grattarola M; Parodi M
    IEEE Trans Biomed Eng; 1997 Dec; 44(12):1282-7. PubMed ID: 9401228
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A theoretical network model to analyse neurogenesis and synaptogenesis in the dentate gyrus.
    Butz M; Lehmann K; Dammasch IE; Teuchert-Noodt G
    Neural Netw; 2006 Dec; 19(10):1490-505. PubMed ID: 17014989
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Computational consequences of experimentally derived spike-time and weight dependent plasticity rules.
    Standage D; Jalil S; Trappenberg T
    Biol Cybern; 2007 Jun; 96(6):615-23. PubMed ID: 17468882
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 34.