BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

709 related articles for article (PubMed ID: 16354381)

  • 1. Spontaneous dynamics of asymmetric random recurrent spiking neural networks.
    Soula H; Beslon G; Mazet O
    Neural Comput; 2006 Jan; 18(1):60-79. PubMed ID: 16354381
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Exact simulation of integrate-and-fire models with synaptic conductances.
    Brette R
    Neural Comput; 2006 Aug; 18(8):2004-27. PubMed ID: 16771661
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Bayesian spiking neurons I: inference.
    Deneve S
    Neural Comput; 2008 Jan; 20(1):91-117. PubMed ID: 18045002
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Learning in realistic networks of spiking neurons and spike-driven plastic synapses.
    Mongillo G; Curti E; Romani S; Amit DJ
    Eur J Neurosci; 2005 Jun; 21(11):3143-60. PubMed ID: 15978023
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Bayesian spiking neurons II: learning.
    Deneve S
    Neural Comput; 2008 Jan; 20(1):118-45. PubMed ID: 18045003
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Event-driven simulations of nonlinear integrate-and-fire neurons.
    Tonnelier A; Belmabrouk H; Martinez D
    Neural Comput; 2007 Dec; 19(12):3226-38. PubMed ID: 17970651
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Speed of synchronization in complex networks of neural oscillators: analytic results based on Random Matrix Theory.
    Timme M; Geisel T; Wolf F
    Chaos; 2006 Mar; 16(1):015108. PubMed ID: 16599774
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Image segmentation by networks of spiking neurons.
    Buhmann JM; Lange T; Ramacher U
    Neural Comput; 2005 May; 17(5):1010-31. PubMed ID: 15829098
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The high-conductance state of cortical networks.
    Kumar A; Schrader S; Aertsen A; Rotter S
    Neural Comput; 2008 Jan; 20(1):1-43. PubMed ID: 18044999
    [TBL] [Abstract][Full Text] [Related]  

  • 10. How noise affects the synchronization properties of recurrent networks of inhibitory neurons.
    Brunel N; Hansel D
    Neural Comput; 2006 May; 18(5):1066-110. PubMed ID: 16595058
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Response of integrate-and-fire neurons to noisy inputs filtered by synapses with arbitrary timescales: firing rate and correlations.
    Moreno-Bote R; Parga N
    Neural Comput; 2010 Jun; 22(6):1528-72. PubMed ID: 20100073
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input.
    Burkitt AN
    Biol Cybern; 2006 Jul; 95(1):1-19. PubMed ID: 16622699
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Mean-driven and fluctuation-driven persistent activity in recurrent networks.
    Renart A; Moreno-Bote R; Wang XJ; Parga N
    Neural Comput; 2007 Jan; 19(1):1-46. PubMed ID: 17134316
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deterministic neural dynamics transmitted through neural networks.
    Asai Y; Guha A; Villa AE
    Neural Netw; 2008 Aug; 21(6):799-809. PubMed ID: 18675536
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Recognition by variance: learning rules for spatiotemporal patterns.
    Barak O; Tsodyks M
    Neural Comput; 2006 Oct; 18(10):2343-58. PubMed ID: 16907629
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Hebbian self-organizing integrate-and-fire networks for data clustering.
    Landis F; Ott T; Stoop R
    Neural Comput; 2010 Jan; 22(1):273-88. PubMed ID: 19764879
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties.
    Burkitt AN
    Biol Cybern; 2006 Aug; 95(2):97-112. PubMed ID: 16821035
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A very simple spiking neuron model that allows for modeling of large, complex systems.
    Lovelace JJ; Cios KJ
    Neural Comput; 2008 Jan; 20(1):65-90. PubMed ID: 18045001
    [TBL] [Abstract][Full Text] [Related]  

  • 19. How to modify a neural network gradually without changing its input-output functionality.
    DiMattina C; Zhang K
    Neural Comput; 2010 Jan; 22(1):1-47. PubMed ID: 19842986
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.
    Siri B; Berry H; Cessac B; Delord B; Quoy M
    Neural Comput; 2008 Dec; 20(12):2937-66. PubMed ID: 18624656
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 36.