These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

108 related articles for article (PubMed ID: 10735529)

  • 1. Controlling activity fluctuations in large, sparsely connected random networks.
    Smith AC; Wu XB; Levy WB
    Network; 2000 Feb; 11(1):63-81. PubMed ID: 10735529
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.
    Tuckwell HC
    J Physiol Paris; 2006; 100(1-3):88-99. PubMed ID: 17064883
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Memory capacity for sequences in a recurrent network with biological constraints.
    Leibold C; Kempter R
    Neural Comput; 2006 Apr; 18(4):904-41. PubMed ID: 16494695
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Input-driven oscillations in networks with excitatory and inhibitory neurons with dynamic synapses.
    Marinazzo D; Kappen HJ; Gielen SC
    Neural Comput; 2007 Jul; 19(7):1739-65. PubMed ID: 17521278
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity--symmetry breaking.
    Gilson M; Burkitt AN; Grayden DB; Thomas DA; van Hemmen JL
    Biol Cybern; 2009 Aug; 101(2):103-14. PubMed ID: 19536559
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. How inhibitory oscillations can train neural networks and punish competitors.
    Norman KA; Newman E; Detre G; Polyn S
    Neural Comput; 2006 Jul; 18(7):1577-610. PubMed ID: 16764515
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges.
    Geisler C; Brunel N; Wang XJ
    J Neurophysiol; 2005 Dec; 94(6):4344-61. PubMed ID: 16093332
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A master equation formalism for macroscopic modeling of asynchronous irregular activity states.
    El Boustani S; Destexhe A
    Neural Comput; 2009 Jan; 21(1):46-100. PubMed ID: 19210171
    [TBL] [Abstract][Full Text] [Related]  

  • 10. On the classification capability of sign-constrained perceptrons.
    Legenstein R; Maass W
    Neural Comput; 2008 Jan; 20(1):288-309. PubMed ID: 18045010
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. Precise capacity analysis in binary networks with multiple coding level inputs.
    Amit Y; Huang Y
    Neural Comput; 2010 Mar; 22(3):660-88. PubMed ID: 19842984
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A learning rule for the emergence of stable dynamics and timing in recurrent networks.
    Buonomano DV
    J Neurophysiol; 2005 Oct; 94(4):2275-83. PubMed ID: 16160088
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Optimizing one-shot learning with binary synapses.
    Romani S; Amit DJ; Amit Y
    Neural Comput; 2008 Aug; 20(8):1928-50. PubMed ID: 18386988
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A new backpropagation learning algorithm for layered neural networks with nondifferentiable units.
    Oohori T; Naganuma H; Watanabe K
    Neural Comput; 2007 May; 19(5):1422-35. PubMed ID: 17381272
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Passive dendritic integration heavily affects spiking dynamics of recurrent networks.
    Ascoli GA
    Neural Netw; 2003; 16(5-6):657-63. PubMed ID: 12850020
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mean field and capacity in realistic networks of spiking neurons storing sparsely coded random memories.
    Curti E; Mongillo G; La Camera G; Amit DJ
    Neural Comput; 2004 Dec; 16(12):2597-637. PubMed ID: 15516275
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Self-tuning of neural circuits through short-term synaptic plasticity.
    Sussillo D; Toyoizumi T; Maass W
    J Neurophysiol; 2007 Jun; 97(6):4079-95. PubMed ID: 17409166
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons.
    Siri B; Quoy M; Delord B; Cessac B; Berry H
    J Physiol Paris; 2007; 101(1-3):136-48. PubMed ID: 18042357
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.