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 *

138 related articles for article (PubMed ID: 25375568)

  • 1. Network dynamics for optimal compressive-sensing input-signal recovery.
    Barranca VJ; Kovačič G; Zhou D; Cai D
    Phys Rev E Stat Nonlin Soft Matter Phys; 2014 Oct; 90(4):042908. PubMed ID: 25375568
    [TBL] [Abstract][Full Text] [Related]  

  • 2. [Dynamic paradigm in psychopathology: "chaos theory", from physics to psychiatry].
    Pezard L; Nandrino JL
    Encephale; 2001; 27(3):260-8. PubMed ID: 11488256
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks.
    Barranca VJ; Zhou D; Cai D
    Phys Rev E; 2016 Jun; 93(6):060201. PubMed ID: 27415190
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics.
    Barranca VJ; Zhou D
    Front Neurosci; 2019; 13():1101. PubMed ID: 31680835
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fast tomographic reconstruction from limited data using artificial neural networks.
    Pelt DM; Batenburg KJ
    IEEE Trans Image Process; 2013 Dec; 22(12):5238-51. PubMed ID: 24108463
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks.
    Barranca VJ
    J Comput Neurosci; 2023 Feb; 51(1):43-58. PubMed ID: 35849304
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Relating observability and compressed sensing of time-varying signals in recurrent linear networks.
    Kafashan M; Nandi A; Ching S
    Neural Netw; 2016 Nov; 83():11-20. PubMed ID: 27541050
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing.
    Liebgott H; Prost R; Friboulet D
    Ultrasonics; 2013 Feb; 53(2):525-33. PubMed ID: 23089222
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Non-linear filtering of ultrasonic signals using neural networks.
    Vicen R; Gil R; Jarabo P; Rosa M; López F; Martínez D
    Ultrasonics; 2004 Apr; 42(1-9):355-60. PubMed ID: 15047311
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Short-term memory capacity in networks via the restricted isometry property.
    Charles AS; Yap HL; Rozell CJ
    Neural Comput; 2014 Jun; 26(6):1198-235. PubMed ID: 24684446
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Echo state property linked to an input: exploring a fundamental characteristic of recurrent neural networks.
    Manjunath G; Jaeger H
    Neural Comput; 2013 Mar; 25(3):671-96. PubMed ID: 23272918
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter.
    Qiu W; Fung KS; Chan FH; Lam FK; Poon PW; Hamernik RP
    IEEE Trans Biomed Eng; 2002 Mar; 49(3):225-32. PubMed ID: 11878313
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Asymptotic behavior and synchronizability characteristics of a class of recurrent neural networks.
    Cebulla C
    Neural Comput; 2007 Sep; 19(9):2492-514. PubMed ID: 17650067
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.
    Tong S; Wang T; Li Y; Zhang H
    IEEE Trans Cybern; 2014 Jun; 44(6):910-21. PubMed ID: 24013830
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
    Yang J; Singh H; Hines EL; Schlaghecken F; Iliescu DD; Leeson MS; Stocks NG
    Artif Intell Med; 2012 Jun; 55(2):117-26. PubMed ID: 22503644
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Slow feature analysis: unsupervised learning of invariances.
    Wiskott L; Sejnowski TJ
    Neural Comput; 2002 Apr; 14(4):715-70. PubMed ID: 11936959
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Nonlinear feedforward networks with stochastic outputs: infomax implies redundancy reduction.
    Nadal JP; Brunel N; Parga N
    Network; 1998 May; 9(2):207-17. PubMed ID: 9861986
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Observer-based adaptive neural network control for nonlinear stochastic systems with time delay.
    Zhou Q; Shi P; Xu S; Li H
    IEEE Trans Neural Netw Learn Syst; 2013 Jan; 24(1):71-80. PubMed ID: 24808208
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Control of nonaffine nonlinear discrete-time systems using reinforcement-learning-based linearly parameterized neural networks.
    Yang Q; Vance JB; Jagannathan S
    IEEE Trans Syst Man Cybern B Cybern; 2008 Aug; 38(4):994-1001. PubMed ID: 18632390
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Autonomous Growing Neural Gas for applications with time constraint: optimal parameter estimation.
    García-Rodríguez J; Angelopoulou A; García-Chamizo JM; Psarrou A; Orts Escolano S; Morell Giménez V
    Neural Netw; 2012 Aug; 32():196-208. PubMed ID: 22386599
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.