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 *

213 related articles for article (PubMed ID: 11128170)

  • 1. Randomly connected sigma-pi neurons can form associator networks.
    Plate TA
    Network; 2000 Nov; 11(4):321-32. PubMed ID: 11128170
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Chaotic pattern transitions in pulse neural networks.
    Kanamaru T
    Neural Netw; 2007 Sep; 20(7):781-90. PubMed ID: 17689050
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Experience-induced neural circuits that achieve high capacity.
    Feldman V; Valiant LG
    Neural Comput; 2009 Oct; 21(10):2715-54. PubMed ID: 19635015
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations.
    Kühn S; Beyn WJ; Cruse H
    Biol Cybern; 2007 May; 96(5):455-70. PubMed ID: 17211628
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Dynamic synchronization and chaos in an associative neural network with multiple active memories.
    Raffone A; van Leeuwen C
    Chaos; 2003 Sep; 13(3):1090-104. PubMed ID: 12946202
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A unified approach to building and controlling spiking attractor networks.
    Eliasmith C
    Neural Comput; 2005 Jun; 17(6):1276-314. PubMed ID: 15901399
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Information-geometric measures as robust estimators of connection strengths and external inputs.
    Tatsuno M; Fellous JM; Amari S
    Neural Comput; 2009 Aug; 21(8):2309-35. PubMed ID: 19538092
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Memory capacity of balanced networks.
    Aviel Y; Horn D; Abeles M
    Neural Comput; 2005 Mar; 17(3):691-713. PubMed ID: 15802011
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Threshold control of chaotic neural network.
    He G; Shrimali MD; Aihara K
    Neural Netw; 2008; 21(2-3):114-21. PubMed ID: 18178377
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Cognitive aspects of chaos in random networks.
    Aiello GL
    Nonlinear Dynamics Psychol Life Sci; 2012 Jan; 16(1):23-35. PubMed ID: 22196110
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Learning only when necessary: better memories of correlated patterns in networks with bounded synapses.
    Senn W; Fusi S
    Neural Comput; 2005 Oct; 17(10):2106-38. PubMed ID: 16105220
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Context-dependent retrieval of information by neural-network dynamics with continuous attractors.
    Tsuboshita Y; Okamoto H
    Neural Netw; 2007 Aug; 20(6):705-13. PubMed ID: 17446042
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Elman topology with sigma-pi units: an application to the modeling of verbal hallucinations in schizophrenia.
    Valle-Lisboa JC; Reali F; Anastasía H; Mizraji E
    Neural Netw; 2005 Sep; 18(7):863-77. PubMed ID: 15935616
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Optimization and applications of echo state networks with leaky-integrator neurons.
    Jaeger H; Lukosevicius M; Popovici D; Siewert U
    Neural Netw; 2007 Apr; 20(3):335-52. PubMed ID: 17517495
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A model of the hippocampal-cortical memory system.
    Pan X; Tsukada M
    Biol Cybern; 2006 Aug; 95(2):159-67. PubMed ID: 16699781
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Learning beyond finite memory in recurrent networks of spiking neurons.
    Tino P; Mills AJ
    Neural Comput; 2006 Mar; 18(3):591-613. PubMed ID: 16483409
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Cooperation of spike timing-dependent and heterosynaptic plasticities in neural networks: a Fokker-Planck approach.
    Zhu L; Lai YC; Hoppensteadt FC; He J
    Chaos; 2006 Jun; 16(2):023105. PubMed ID: 16822008
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Persistent neural states: stationary localized activity patterns in nonlinear continuous n-population, q-dimensional neural networks.
    Faugeras O; Veltz R; Grimbert F
    Neural Comput; 2009 Jan; 21(1):147-87. PubMed ID: 19431281
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 11.