BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

234 related articles for article (PubMed ID: 30662939)

  • 1. 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]  

  • 2. 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]  

  • 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 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]  

  • 5. Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation.
    Augustin M; Ladenbauer J; Baumann F; Obermayer K
    PLoS Comput Biol; 2017 Jun; 13(6):e1005545. PubMed ID: 28644841
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Simplicity and efficiency of integrate-and-fire neuron models.
    Plesser HE; Diesmann M
    Neural Comput; 2009 Feb; 21(2):353-9. PubMed ID: 19431263
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Learning in neural networks by reinforcement of irregular spiking.
    Xie X; Seung HS
    Phys Rev E Stat Nonlin Soft Matter Phys; 2004 Apr; 69(4 Pt 1):041909. PubMed ID: 15169045
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics.
    Brader JM; Senn W; Fusi S
    Neural Comput; 2007 Nov; 19(11):2881-912. PubMed ID: 17883345
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Mean-field theory of a plastic network of integrate-and-fire neurons.
    Chen CC; Jasnow D
    Phys Rev E Stat Nonlin Soft Matter Phys; 2010 Jan; 81(1 Pt 1):011907. PubMed ID: 20365399
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.
    Schwalger T; Deger M; Gerstner W
    PLoS Comput Biol; 2017 Apr; 13(4):e1005507. PubMed ID: 28422957
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Synaptic dynamics: linear model and adaptation algorithm.
    Yousefi A; Dibazar AA; Berger TW
    Neural Netw; 2014 Aug; 56():49-68. PubMed ID: 24867390
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Effects of random external background stimulation on network synaptic stability after tetanization: a modeling study.
    Chao ZC; Bakkum DJ; Wagenaar DA; Potter SM
    Neuroinformatics; 2005; 3(3):263-80. PubMed ID: 16077162
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal networks.
    Madadi Asl M; Valizadeh A; Tass PA
    Chaos; 2018 Oct; 28(10):106308. PubMed ID: 30384625
    [TBL] [Abstract][Full Text] [Related]  

  • 18. The effects of dynamical synapses on firing rate activity: a spiking neural network model.
    Khalil R; Moftah MZ; Moustafa AA
    Eur J Neurosci; 2017 Nov; 46(9):2445-2470. PubMed ID: 28921686
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Event-driven simulations of a plastic, spiking neural network.
    Chen CC; Jasnow D
    Phys Rev E Stat Nonlin Soft Matter Phys; 2011 Sep; 84(3 Pt 1):031908. PubMed ID: 22060404
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Symmetry breaking in two interacting populations of quadratic integrate-and-fire neurons.
    Ratas I; Pyragas K
    Phys Rev E; 2017 Oct; 96(4-1):042212. PubMed ID: 29347512
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.