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.
23. Synaptic transmission of chaotic spike trains between primary afferent fiber and spinal dorsal horn neuron in the rat. Wan YH; Jian Z; Wen ZH; Wang YY; Han S; Duan YB; Xing JL; Zhu JL; Hu SJ Neuroscience; 2004; 125(4):1051-60. PubMed ID: 15120864 [TBL] [Abstract][Full Text] [Related]
24. Parameter estimation in spiking neural networks: a reverse-engineering approach. Rostro-Gonzalez H; Cessac B; Vieville T J Neural Eng; 2012 Apr; 9(2):026024. PubMed ID: 22419215 [TBL] [Abstract][Full Text] [Related]
25. Detecting dependencies between spike trains of pairs of neurons through copulas. Sacerdote L; Tamborrino M; Zucca C Brain Res; 2012 Jan; 1434():243-56. PubMed ID: 21981802 [TBL] [Abstract][Full Text] [Related]
26. Stochastic resonance can enhance information transmission in neural networks. Kawaguchi M; Mino H; Durand DM IEEE Trans Biomed Eng; 2011 Jul; 58(7):1950-8. PubMed ID: 21435971 [TBL] [Abstract][Full Text] [Related]
27. Self-control with spiking and non-spiking neural networks playing games. Christodoulou C; Banfield G; Cleanthous A J Physiol Paris; 2010; 104(3-4):108-17. PubMed ID: 19944157 [TBL] [Abstract][Full Text] [Related]
28. 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]
29. 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]
30. Fitting a stochastic spiking model to neuronal current injection data. Shinomoto S Neural Netw; 2010 Aug; 23(6):764-9. PubMed ID: 20478693 [TBL] [Abstract][Full Text] [Related]
31. Variable timescales of repeated spike patterns in synfire chain with Mexican-hat connectivity. Hamaguchi K; Okada M; Aihara K Neural Comput; 2007 Sep; 19(9):2468-91. PubMed ID: 17650066 [TBL] [Abstract][Full Text] [Related]
32. 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]
34. 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]
35. Recognition by variance: learning rules for spatiotemporal patterns. Barak O; Tsodyks M Neural Comput; 2006 Oct; 18(10):2343-58. PubMed ID: 16907629 [TBL] [Abstract][Full Text] [Related]
36. 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]
37. 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]
38. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Burkitt AN Biol Cybern; 2006 Jul; 95(1):1-19. PubMed ID: 16622699 [TBL] [Abstract][Full Text] [Related]
39. Propagation of spiking regularity and double coherence resonance in feedforward networks. Men C; Wang J; Qin YM; Deng B; Tsang KM; Chan WL Chaos; 2012 Mar; 22(1):013104. PubMed ID: 22462980 [TBL] [Abstract][Full Text] [Related]
40. Solution methods for a new class of simple model neurons. Humphries MD; Gurney K Neural Comput; 2007 Dec; 19(12):3216-25. PubMed ID: 17970650 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]