BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

299 related articles for article (PubMed ID: 23777517)

  • 1. A principled dimension-reduction method for the population density approach to modeling networks of neurons with synaptic dynamics.
    Ly C
    Neural Comput; 2013 Oct; 25(10):2682-708. PubMed ID: 23777517
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling.
    Ly C; Tranchina D
    Neural Comput; 2007 Aug; 19(8):2032-92. PubMed ID: 17571938
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Population density methods for stochastic neurons with realistic synaptic kinetics: firing rate dynamics and fast computational methods.
    Apfaltrer F; Ly C; Tranchina D
    Network; 2006 Dec; 17(4):373-418. PubMed ID: 17162461
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex.
    Lang S; Dercksen VJ; Sakmann B; Oberlaender M
    Neural Netw; 2011 Nov; 24(9):998-1011. PubMed ID: 21775101
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Statistical physics approaches to neuronal network dynamics.
    Cai D; Tao L
    Sheng Li Xue Bao; 2011 Oct; 63(5):453-62. PubMed ID: 22002236
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. An Efficient Population Density Method for Modeling Neural Networks with Synaptic Dynamics Manifesting Finite Relaxation Time and Short-Term Plasticity.
    Huang CH; Lin CK
    eNeuro; 2018; 5(6):. PubMed ID: 30662939
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach.
    Ly C; Tranchina D
    Neural Comput; 2009 Feb; 21(2):360-96. PubMed ID: 19431264
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A population density approach that facilitates large-scale modeling of neural networks: extension to slow inhibitory synapses.
    Nykamp DQ; Tranchina D
    Neural Comput; 2001 Mar; 13(3):511-46. PubMed ID: 11244554
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Synchronization of an excitatory integrate-and-fire neural network.
    Dumont G; Henry J
    Bull Math Biol; 2013 Apr; 75(4):629-48. PubMed ID: 23435645
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. What the training of a neuronal network optimizes.
    Tabor Z
    Phys Rev E Stat Nonlin Soft Matter Phys; 2007 Sep; 76(3 Pt 1):031905. PubMed ID: 17930269
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.
    Ly C
    J Comput Neurosci; 2015 Dec; 39(3):311-27. PubMed ID: 26453404
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Correlation between uncoupled conductance-based integrate-and-fire neurons due to common and synchronous presynaptic firing.
    Stroeve S; Gielen S
    Neural Comput; 2001 Sep; 13(9):2005-29. PubMed ID: 11516355
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 15.