Specificity of Bionoise

Glossary Bio
Acronyms Bio


“Hopefully, we can get some noise on our side this time.”

(Jerome Bettis)


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  2. Wang Qi, Gong Yubing: Effect of Autaptic Activity on Intrinsic Coherence Resonance in Newman-Watts Networks of Stochastic Hodgkin-Huxley Neurons. Fluct. & Noise Lett., Vol. 15, no. 2, 2016, Article # 1650016. DOI 10.1142/S0219477516500164

  3. Wang Qi, Gong Yubing: Multiple coherence resonance and synchronization transitions induced by autaptic delay in Newman-Watts neuron networks. Applied Mathematical Modelling, Vol. 40, no. 15-16, 2016, pp. 71477155.

  4. Onorato I., D'Alessandro G., Di Castro M.A., et al.: Noise Enhances Action Potential Generation in Mouse Sensory Neurons via Stochastic Resonance. PLoS One, Vol. 11, no. 8, 2016, Article # e0160950. DOI 10.1371/journal.pone.0160950

  5. Wang Hengtong, Chen Yong: Response of autaptic Hodgkin-Huxley neuron with noise to subthreshold sinusoidal signals. Physica A - Statistical Mechanics and its Applications, Vol. 462, 2016, pp. 321329. DOI 10.1016/j.physa.2016.06.019

  6. Kang Qi, Huang BingYao, Zhou MengChu: Dynamic Behavior of Artificial Hodgkin-Huxley Neuron Model Subject to Additive Noise. IEEE Trans on Cybernetics, Vol. 46, no. 9, 2016, pp. 20832093. DOI 10.1109/TCYB.2015.2464106

  7. Zhe Sun, Micheletto R.: Noise influence on spike activation in a Hindmarsh-Rose small-world neural network. Journal of Physics A - Mathematical and Theoretical, Vol. 49, no. 28, 2016, Article # 285601. DOI 10.1088/1751-8113/49/28/285601

  8. Chalk M., Gutkin B., Deneve S.: Neural oscillations as a signature of efficient coding in the presence of synaptic delays. ELIFE, Vol. 5, 2016, Article # e13824. DOI 10.7554/eLife.11324

  9. Xie Huijuan, Gong Yubing, Wang Qi: Effect of spike-timing-dependent plasticity on coherence resonance and synchronization transitions by time delay in adaptive neuronal networks. European Physical Journal B, Vol. 89, no. 7, 2016, pp. 17, Article # 161. DOI 10.1140/epjb/e2016-70282-4

  10. Sun Xiao-Juan, Li Guo-Fang: Stochastic multi-resonance induced by partial time delay in a Watts-Strogatz small-world neuronal network. Acta Physica Sinica, Vol. 65, no. 12, 2016, Article # 120502. DOI 10.7498/aps.65.120502

  11. Fabing Duan, Chapeau-Blondeau F., Derek A.: Encoding efficiency of suprathreshold stochastic resonance on stimulus-specific information. Physics Lett. A, Vol. 380, no. 1-2, 2016, pp. 33 – 39. DOI 10.1016/j.physleta.2015.09.043

  12. Liyan Xu, Fabing Duan, Derek A., McDonnell M.D.: Optimal weighted suprathreshold stochastic resonance with multigroup saturating sensors. Physica A: Statistical Mechanics and its Applications, Vol. 457, 2016, pp. 348 – 355. DOI 10.1016/j.physa.2016.03.064

  13. Qiqing Zhai, Youguo Wang: Optimal and Suboptimal Noises Enhancing Mutual Information in Threshold System. Fluct. Noise Lett., Vol. 15, no. 02, 2016, Article # 1650015. DOI 10.1142/S0219477516500152

  14. Audhkhasi Kartik, Osoba Osonde, Kosko B.: Noise-enhanced convolutional neural networks. Neural Networks, Vol. 78, Special no. SI, 2016, pp. 15 23. DOI 10.1016/j.neunet.2015.09.014

  15. Sanchez A.D., Izus G.G.: Noise-sustained synchronization of electrically coupled FitzHugh-Nagumo networks under counterphase external forcing. Physics Lett. A, Vol. 380, no. 2223, 2016, pp. 1964-1970. DOI 10.1016/j.physleta.2016.04.017

  16. Yu Lianchun, Zhang Chi, Liu Liwei, et al.: Energy-efficient population coding constrains network size of a neuronal array system. Scientific Reports, Vol. 6, 2016, Article # 19369. DOI 10.1038/srep19369

  17. Guo Daqing, Wu Shengdun, Chen Mingming, et al.: Regulation of Irregular Neuronal Firing by Autaptic Transmission. Scientific Reports, Vol. 6, 2016, Article # 26096. DOI 10.1038/srep26096

  18. Yonekura Shogo, Kuniyoshi Yasuo, Kawaguchi Yoichiro: Growth of stochastic resonance in neuronal ensembles with the input signal intensity. Physical Review E, Vol. 93, no. 3, 2016, Article # 039903. DOI 10.1103/PhysRevE.93.039903

  19. Djeundam S.R. Dtchetgnia, Yamapi R., Filatrella G., et al.: Dynamics of Disordered Network of Coupled Hindmarsh-Rose Neuronal Models. Int. J. of Bifurcation & Chaos, Vol. 26, no. 3, 2016, Article # 1650048. DOI 10.1142/S0218127416500486

  20. Yilmaz Ergin, Baysal Veli, Perc Matjaz, et al.: Enhancement of pacemaker induced stochastic resonance by an autapse in a scale-free neuronal network. Science China - Technological Sciences, Vol. 59, no. 3, 2016, pp. 364370. DOI 10.1007/s11431-015-5984-z

  21. Yilmaz Ergin, Baysal Veli, Ozer Mahmut, et al.: Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks. Physica A - Statistical Mechanics and its Applications, Vol. 444, 2016, pp. 538546. DOI 10.1016/j.physa.2015.10.054

  22. Wang ZhanQing, Xu Yong, Yang Hui: Levy noise induced stochastic resonance in an FHN model. Science China - Technological Sciences, Vol. 59, no. 3, 2016, pp. 371375. DOI 10.1007/s11431-015-6001-2

  23. Chen YueLing, Yu LianChun, Chen Yong: Reliability of weak signals detection in neurons with noise. Science China - Technological Sciences, Vol. 59, no. 3, 2016, pp. 411417. DOI 10.1007/s11431-015-6000-3

  24. Li Dongxi, Hu Bing, Wang Jia, et al.: Coherence resonance in the two-dimensional neural map driven by non-Gaussian colored noise. Int. J. of Modern Physics B, Vol. 30, no. 5, 2016, Article # 1650012. DOI 10.1142/S0217979216500120

  25. Wang Jiang, Han Ruixue, Wei Xilei, et al.: Weak signal detection and propagation in diluted feed-forward neural network with recurrent excitation and inhibition. Int. J. of Modern Physics B, Vol. 30, no. 2, 2016, Article # 1550253. DOI 10.1142/S0217979215502537

  26. Yu Lianchun, Zhang Chi, Liu Liwei, Yu Yuguo: Energy-efficient population coding constrains network size of a neuronal array system. Scientific Reports, Vol. 6, 2016, Article # 19369. DOI 10.1038/srep19369

  27. Li X.L., Ning L.J.: Effect of correlation in FitzHugh-Nagumo model with non-Gaussian noise and multiplicative signal. Indian J. of Physics, Vol. 90, no. 1, 2016, pp. 9198. DOI 10.1007/s12648-015-0717-5

  28. Antal A., Herrmann C.S.: Transcranial Alternating Current and Random Noise Stimulation: Possible Mechanisms. Neural Plasticity, 2016, Article # 3616807. DOI 10.1155/2016/3616807

  29. Paulus W., Nitsche M.A., Antal A.: Application of Transcranial Electric Stimulation (tDCS, tACS, tRNS) From Motor-Evoked Potentials Towards Modulation of Behaviour. European Psychologist, Vol. 21, no. 1, Special no. SI, 2016, pp. 414. DOI 10.1027/1016-9040/a000242

  30. Kaut O., Brenig D., Marek M., et al.: Postural Stability in Parkinson's Disease Patients Is Improved after Stochastic Resonance Therapy. Parkinsons Disease, 2016, Article # 7948721. DOI 10.1155/2016/7948721

  31. Haitao Yu, Xinmeng Guo, Jiang Wang, Chen Liu, Bin Deng, Xile Wei: Adaptive stochastic resonance in self-organized small-world neuronal networks with time delay. Communications in Nonlinear Science & Numerical Simulation, Vol. 29, no. 1-3, 2015, pp. 346358. DOI 10.1016/j.cnsns.2015.05.017

  32. Voronenko S.O, Stannat W., Lindner B.: Shifting Spike Times or Adding and Deleting Spikes - How Different Types of Noise Shape Signal Transmission in Neural Populations. Journal of Mathematical Neuroscience, Vol. 5, no. 1, 2015, pp. 1. DOI 10.1186/2190-8567-5-1

  33. Kim Sang-Yoon, Lim Woochang: Frequency-domain order parameters for the burst and spike synchronization transitions of bursting neurons. Cognitive Neurodynamics, Vol. 9, no. 4, 2015, pp. 411421. DOI 10.1007/s11571-015-9334-4

  34. Yu Haitao, Guo Xinmeng, Wang Jiang, et al.: Adaptive stochastic resonance in self-organized small-world neuronal networks with time delay. Communications in Nonlinear Science & Numerical Simulation, Vol. 29, no. 1-3, 2015, pp. 346358. DOI 10.1016/j.cnsns.2015.05.017

  35. Zhang Gang, Hu Tao, Zhang Tian-Qi: Characteristic analysis of power function type monostable stochastic resonance with Levy noise. Acta Physica Sinica, Vol. 64, no. 22, 2015, Article # 220502. DOI 10.7498/aps.64.220502

  36. Mendez-Balbuena I., Huidobro N., Silva M., et al.: Effect of mechanical tactile noise on amplitude of visual evoked potentials: multisensory stochastic resonance. Journal of Neurophysiology, Vol. 114, no. 4, 2015, pp. 21322143. DOI 10.1152/jn.00457.2015

  37. Nobukawa Sou, Nishimura Haruhiko, Yamanishi Teruya, et al.: Analysis of Chaotic Resonance in Izhikevich Neuron Model. PLoS One, Vol. 10, no. 9, 2015, Article # e0138919. DOI 10.1371/journal.pone.0138919

  38. Zhang Xuejuan, Li Hui, Chen Jianchun, et al.: The impact of channel and external synaptic noises on spatial and temporal coherence in neuronal networks. Neurocomputing, Vol. 164, 2015, pp. 230239. DOI 10.1016/j.neucom.2015.02.066

  39. Buonocore A., Caputo L., Nobile A.G., et al.: Restricted Ornstein-Uhlenbeck process and applications in neuronal models with periodic input signals. Journal of Computational & Applied Mathematics, Vol. 285, 2015, pp. 5971. DOI 10.1016/j.cam.2015.01.042

  40. da Silva, L.A., Vilela, R.D.: Colored noise and memory effects on formal spiking neuron models. Physical Review E, Vol. 91, no. 6, 2015, Article # 062702. DOI 10.1103/PhysRevE.91.062702

  41. Yong Xu, Jing Feng, Wei Xu, et al.: Probability Density Transitions in the FitzHugh-Nagumo Model with Levy Noise. CMES - Computer Modeling in Eng. & Sciences, Vol. 106, no. 5, 2015, pp. 309322. http://www.techscience.com/doi/10.3970/cmes.2015.106.309.pdf

  42. Zhang Huiqing, Yang Tingting, Xu Yong, Xu Wei: Parameter dependence of stochastic resonance in the FitzHugh-Nagumo neuron model driven by trichotomous noise. European Physical Journal B, Vol. 88, no. 5, 2015, Article # 125. DOI 10.1140/epjb/e2015-50865-3

  43. Paffi A., Camera F., Apollonio F., et al.: Restoring the encoding properties of a stochastic neuron model by an exogenous noise. Frontiers in Computational Neuroscience, Vol. 9, 2015, Article # 42. DOI 10.3389/fncom.2015.00042

  44. Wang Nan, Song Aiguo: Parameter-induced logical stochastic resonance. Neurocomputing, Vol. 155, 2015, pp. 8083. DOI 10.1016/j.neucom.2014.12.045

  45. Silva I.G., Rosso O.A., Vermelho M.V.D., Lyra M.L.: Ghost stochastic resonance induced by a power-law distributed noise in the FitzHugh-Nagumo neuron model. Communications in Nonlinear Science & Numerical Simulation, Vol. 22, no. 1-3, 2015, pp. 641649. DOI 10.1016/j.cnsns.2014.06.050

  46. Metzen M.G., Avila-Akerberg O., Chacron M.J.: Coding stimulus amplitude by correlated neural activity. Physical Review E, Vol. 91, no. 4, 2015, Article # 042717. DOI 10.1103/PhysRevE.91.042717

  47. Zhou Wen-Liang, Short S.M., Rich M.T., et al.: Branch specific and spike-order specific action potential invasion in basal, oblique, and apical dendrites of cortical pyramidal neurons. Neurophotonics, Vol. 2, no. 2, 2015, Article # 021006. DOI 10.1117/1.NPh.2.2.021006

  48. Torres J.J., Elices I., Marro J.: Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons. PLoS One, Vol. 10, no. 3, 2015, Article # e0121156. DOI 10.1371/journal.pone.0121156

  49. Li X.L., Ning L.J.: Stochastic resonance in FizHugh-Nagumo model driven by multiplicative signal and non-Gaussian noise. Indian J. of Physics, Vol. 89, no. 2, 2015, pp. 189194. DOI 10.1007/s12648-014-0537-z

  50. Danziger Z., Grill W.M.: A neuron model of stochastic resonance using rectangular pulse trains. Journal of Computational Neuroscience, Vol. 38, no. 1, 2015, pp. 5366. DOI 10.1007/s10827-014-0526-4

  51. Voronenko S.O., Stannat W., Lindner B.: Shifting Spike Times or Adding and Deleting Spikes-How Different Types of Noise Shape Signal Transmission in Neural Populations. Journal of Mathematical Neuroscience, Vol. 5, 2015, Article # 1. DOI 10.1186/2190-8567-5-1

  52. McDonnell M.D., Iannella N., To Minh-Son, et al.: A review of methods for identifying stochastic resonance in simulations of single neuron models. Network - Computation in Neural Syst., Vol. 26, no. 2, 2015, pp. 3571. DOI 10.3109/0954898X.2014.990064

  53. Qi Wang, Yubing Gong, Yanan Wu: Optimization of Intrinsic Stochastic Resonance in Adaptive Newman–Watts Network of Channel Blocked Hodgkin–Huxley Neurons. Fluct. Noise Lett., Vol. 13, no. 04, 2014, Article # 1450026. DOI 10.1142/S0219477514500266

  54. Gjorgjieva J., Mease R.A., Moody W.J., et al.: Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks. PLoS Computational Biology, Vol. 10, no. 12, 2014, Article # e1003962. DOI 10.1371/journal.pcbi.1003962

  55. Casson A.J.: Artificial Neural Network classification of operator workload with an assessment of time variation and noise-enhancement to increase performance. Frontiers in Neuroscience, Vol. 8, 2014, Article # 372. DOI 10.3389/fnins.2014.00372

  56. Lopes M.A., Lee K.-E., Goltsev A.V., et al.: Noise-enhanced nonlinear response and the role of modular structure for signal detection in neuronal networks. Physical Review E, Vol. 90, no. 5, 2014, Article # 052709. DOI 10.1103/PhysRevE.90.052709

  57. Deng Bin, Wang Lin, Wang Jiang, et al.: Endogenous fields enhanced stochastic resonance in a randomly coupled neuronal network. Chaos Solitons & Fractals, Vol. 68, 2014, pp. 3039. DOI 10.1016/j.chaos.2014.07.006

  58. Karbasi A., Salavati A.H., Shokrollahi A., et al.: Noise Facilitation in Associative Memories of Exponential Capacity. Neural Computation, Vol. 26, no. 11, 2014, pp. 24932526. DOI 10.1162/NECO_a_00655

  59. Wang Can Jun, Yang Ke Li, Qu Shi Xian: Stochastic resonance in a discrete neuron with time delay and two different modulation signals. Physica Scripta, Vol. 89, no. 10, 2014, Article # 105001. DOI 10.1088/0031-8949/89/10/105001

  60. Wu Yanan, Gong Yubing, Wang Qi: Noise-induced synchronization transitions in neuronal network with delayed electrical or chemical coupling. European Physical Journal B, Vol. 87, no. 9, 2014, Article # 198. DOI 10.1140/epjb/e2014-50437-1

  61. Yu Haitao, Guo Xinmeng, Wang Jiang, et al.: Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks. Chaos, Vol. 24, no. 3, 2014, Article # 033125. DOI 10.1063/1.4893773

  62. Hunsberger E., Scott M., Eliasmith C.: The Competing Benefits of Noise and Heterogeneity in Neural Coding. Neural Computation, Vol. 26, no. 8, 2014, pp. 16001623. DOI 10.1162/NECO_a_00621

  63. Rausch V.H., Bauch E.M., Bunzeck N.: White Noise Improves Learning by Modulating Activity in Dopaminergic Midbrain Regions and Right Superior Temporal Sulcus. Journal of Cognitive Neuroscience, Vol. 26, no. 7, 2014 pp. 14691480. DOI 10.1162/jocn_a_00537

  64. Li Huan, Wang You-Guo: Noise-enhanced information transmission of a non-linear multilevel threshold neural networks system. Acta Physica Sinica, Vol. 63, no. 12, 2014, Article # 120506. DOI 10.7498/aps.63.120506

  65. Lu Qiang, Tian Juan: Synchronization and stochastic resonance of the small-world neural network based on the CPG. Cognitive Neurodynamics, Vol. 8, no. 3, 2014, pp. 217226. DOI 10.1007/s11571-013-9275-8

  66. dell'Erba M.G., Cascallares G., Sanchez A.D., et al.: Noise-sustained synchronization in a FitzHugh-Nagumo ring with electrical phase-repulsive coupling. European Physical Journal B, Vol. 87, no. 4, 2014, Article # 82. DOI 10.1140/epjb/e2014-41029-2

  67. Liu Ying, Xu Xinmin: Stochastic and Coherence Resonance in a Dressed Neuron Model. Int. J. of Bifurcation & Chaos, Vol. 24, no. 4, 2014, Article # 1450052. DOI 10.1142/S0218127414500527

  68. Zheng Yanhong, Wang Qingyun, Danca M.-F.: Noise induced complexity: patterns and collective phenomena in a small-world neuronal network. Cognitive Neurodynamics, Vol. 8, no. 2, 2014, pp. 143149. DOI 10.1007/s11571-013-9257-x

  69. Sirovich R., Sacerdote L., Villa A.E.P.: Cooperative Behavior in a Jump Diffusion Model for a Simple Network of Spiking Neurons. Mathematical Biosciences and Engineering, Vol. 11, no. 2, Special no. SI, 2014, pp. 385401. DOI 10.3934/mbe.2014.11.385

  70. Liu Chen, Wang Jiang, Yu Haitao, et al.: The effects of time delay on the stochastic resonance in feed-forward-loop neuronal network motifs. Communications in Nonlinear Science & Numerical Simulation, Vol. 19, no. 4, 2014, pp. 10881096. DOI 10.1016/j.cnsns.2013.08.021

  71. Yu Lianchun, Liu Liwei: Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals. Physical Review E, Vol. 89, no. 3, 2014, Article # 032725. DOI 10.1103/PhysRevE.89.032725

  72. Duan Fabing, Chapeau-Blondeau F., Abbott D.: Stochastic Resonance with Colored Noise for Neural Signal Detection. PLoS One, Vol. 9, no. 3, 2014, Article # e91345. DOI 10.1371/journal.pone.0091345

  73. Iliopoulos F., Nierhaus T., Villringer A.: Electrical noise modulates perception of electrical pulses in humans: sensation enhancement via stochastic resonance. Journal of Neurophysiology, Vol. 111, no. 6, 2014, pp. 12381248. DOI 10.1152/jn.00392.2013

  74. Wang Jiang, Guo Xinmeng, Yu Haitao, et al.: Stochastic resonance in small-world neuronal networks with hybrid electrical-chemical synapses. Chaos Solitons & Fractals, Vol. 60, 2014, pp. 4048. DOI 10.1016/j.chaos.2014.01.005

  75. Karbasi A., Salavati A.H., Shokrollahi A., et al.: Noise Facilitation in Associative Memories of Exponential Capacity. Neural Computation, Vol. 26, no. 11, 2014, pp. 2493 2526. DOI 10.1162/NECO_a_00655

  76. Liu Chen, Wang Jiang, Yu Haitao, Deng Bin, Tsang K.M, Chan W.L, Wong Y.K: The effects of time delay on the stochastic resonance in feed-forward-loop neuronal network motifs. Communications in Nonlinear Science and Numerical Simulation, Vol. 19, no. 4, 2014, pp. 1088 – 1096. DOI 10.1016/j.cnsns.2013.08.021

  77. Yang X. L., Jia Y. B., Zhang L.: Impact of bounded noise and shortcuts on the spatiotemporal dynamics of neuronal networks. Physica A - Statistical Mechanics and its Applications, Vol. 393, 2014, pp. 617 – 623. DOI 10.1016/j.physa.2013.09.021

  78. Liu Ying, Li Chunguang: Stochastic resonance in feedforward-loop neuronal network motifs in astrocyte field. Journal of Theoretical Biology, Vol. 335, 2013, pp. 265 – 275. DOI 10.1016/j.jtbi.2013.07.007

  79. Noguchi T., Torikai H.: Ghost Stochastic Resonance From an Asynchronous Cellular Automaton Neuron Model. IEEE Trans on CAS II: Express Briefs, Vol. 60, no. 2, 2013, pp 111 – 115. DOI 10.1109/TCSII.2012.2235015

  80. Yu Haitao, Wang Jiang, Du Jiwei, Deng Bin, Wei Xile, Liu Chen: Effects of time delay on the stochastic resonance in small-world neuronal networks. Chaos, Vol. 23, no. 1, 2013, Article # 013128. DOI 10.1063/1.479082

  81. Wu Xinyi, Ma Jun, Li Fan, Jia Ya: Development of spiral wave in a regular network of excitatory neurons due to stochastic poisoning of ion channels. Communications in Nonlinear Science and Numerical Simulation, Vol. 18, no. 12, 2013, pp. 3350 – 3364. DOI 10.1016/j.cnsns.2013.05.011

  82. Uzuntarla M.: Inverse stochastic resonance induced by synaptic background activity with unreliable synapses. Physics Lett. A, Vol. 377, no. 38, 2013, pp. 2585 – 2589. DOI 10.1016/j.physleta.2013.08.009

  83. Yilmaz E., Uzuntarla M., Ozer M., Perc M.: Stochastic resonance in hybrid scale-free neuronal networks. Physica A - Statistical Mechanics and its Applications, Vol. 392, no. 22, 2013, pp. 5735 – 5741. DOI 10.1016/j.physa.2013.07.011

  84. Paprocki B., Szczepanski J.: Transmission efficiency in ring, brain inspired neuronal networks. Information and energetic aspects. Brain Research,  Vol. 1536, Special no., 2013, pp. 135 – 143. DOI 10.1016/j.brainres.2013.07.024

  85. Qin Ying-Mei, Wang Jiang, Men Cong, Chan Wai-Lok , Wei Xi-Le: Control of synchronization and spiking regularity by heterogenous aperiodic high-frequency signal in coupled excitable systems. Communications in Nonlinear Science and Numerical Simulation, Vol. 18, no. 10, 2013, pp. 2775 – 2782. DOI 10.1016/j.cnsns.2013.02.010

  86. Miniussi C., Harris J. A., Ruzzoli M.: Modelling non-invasive brain stimulation in cognitive neuroscience. Neuroscience and Biobehavioral Reviews, Vol. 37, no. 8, 2013, pp. 1702 – 1712. DOI 10.1016/j.neubiorev.2013.06.014

  87. Gong Yubing, Xu Bo, Wu Yanan: Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks. Chaos, Vol. 23, no. 3, 2013, Article # 033105. DOI 10.1063/1.4813224

  88. Sun Jianbing, Deng Bin, Liu Chen, Yu Haitao, Wang Jiang, Wei Xile, Zhao Jia: Vibrational resonance in neuron populations with hybrid synapses. Applied Mathematical Modelling, Vol. 37, no. 9, 2013, pp. 6311 – 6324. DOI 10.1016/j.apm.2013.01.007

  89. Osoba O., Kosko B.: Noise-enhanced clustering and competitive learning algorithms. Neural Networks, Vol. 37, 2013, pp. 132 – 140. DOI 10.1016/j.neunet.2012.09.012 http://sipi.usc.edu/~kosko/Noisy-Clustering-Neural-Networks.pdf

  90. Tatchim Bemmo D., Siewe Siewe M., Tchawoua C.: Combined effects of correlated bounded noises and weak periodic signal input in the modified FitzHugh–Nagumo neural model. Communications in Nonlinear Science and Numerical Simulation, Vol. 18, no. 5, 2013, pp. 1275 – 1287. DOI 10.1016/j.cnsns.2012.09.016

  91. Koutsou A., Kanev J., Christodoulou C.: Measuring input synchrony in the Omstein-Uhlenbeck neuronal model through input parameter estimation. Int. Workshop on Neural Coding (NC), 2012 DOI 10.1016/j.brainres.2013.05.012

  92. Guo Daqing, Li Chunguang: Stochastic resonance in Hodgkin–Huxley neuron induced by unreliable synaptic transmission. Journal of Theoretical Biology, Vol. 308, 2012, pp. 105 – 114. PMID 22687443

  93. Nobukawa S., Nishimura H.: Synchronous spike propagation in Izhikevich neuron system with spike-timing dependent plasticity. Proc. of SICE Annual Conference (SICE), 2012, pp. 453 – 458. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6318482&isnumber=6318306

  94. H. C.Tuckwell, J. Jost: Analysis of inverse stochastic resonance and the long-term firing of Hodgkin–Huxley neurons with Gaussian white noise. Physica A, Vol. 391, no. 22, 2012, pp. 5311 – 5325. DOI 10.1016/j.physa.2012.06.019

  95. Hailing W., Yingle F., Ke C.: Enhancement of Low-dose Lung CT Image Based on Stochastic Resonance of FHN Neurons. Space Medicine and Medical Engineering, Vol. 25, no. 2, 2012, pp. 121 – 125.

  96. Hardy L.C., Levine D.S., Dahai Liu: Neurohydrodynamics as a heuristic mechanism for cognitive processes in decision-making. Int. Joint Conference on Neural Networks (IJCNN), 2012, pp. 1 – 10. DOI 10.1109/IJCNN.2012.6252375

  97. Kawaguchi M., Mino H., Durand D.M.: Stochastic Resonance Can Enhance Information Transmission in Neural Networks. IEEE Trans on Biomedical Eng., Vol. 58, no. 7, 2011, pp. 1950 – 1958. DOI 10.1109/TBME.2011.2126571

  98. McDonnell M.D., Ward L.M.: The benefits of noise in neural systems: bridging theory and experiment. Nature Reviews Neuroscience, Vol. 12, 2011, pp. 415 – 426. DOI 10.1038/nrn3061

  99. Minato Kawaguchi, Hiroyuki Mino, Keiko Momose, D. M. Durand: Stochastic Resonance with a Mixture of Sub- and Supra-threshold Stimuli in a Population of Neuron Models. 3rd Annual Int. Conf. of the IEEE EMBS, Boston, Massachusetts USA, 2011 pp 7328 – 7331. DOI 10.1109/IEMBS.2011.6091709

  100. Kawaguchi M., Mino H., Momose K., Durand D.M.: Stochastic resonance with a mixture of sub-and supra-threshold stimuli in a population of neuron models. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2011, pp. 7328 – 7331. DOI 10.1109/IEMBS.2011.6091709

  101. Nobukawa S., Nishimura H., Yamanishi T., Jian-Qin Liu: Signal response efficiency in Izhikevich neuron model. Proc. of SICE Annual Conference (SICE), 2011, pp. 1242 – 1247.  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6060524&isnumber=6060171

  102. Osoba O., Mitaim S., Kosko B.: Noise benefits in the expectation-maximization algorithm: New theorems and models. Int. Joint Conference on Neural Networks (IJCNN), 2011, pp. 3178 – 3183. DOI 10.1109/IJCNN.2011.6033642

  103. Ushakov Y.V., Karandasov E.S., Dubkov A.A.: Ghost stochastic resonance in the model of neural auditory system. 21st Int. Conf. on Noise and Fluctuations (ICNF), 12-16 June 2011, pp 493 – 494. DOI 10.1109/ICNF.2011.5994378

  104. Applebaum D.: Extending Stochastic Resonance for Neuron Models to General Levy Noise. IEEE Trans. on Neural Networks, vol 20, no 12, 2009, pp. 1993 – 1995. DOI 10.1109/TNN.2009.2033183

  105. Ozer M., Matjaž Perc, M. Uzuntarla: Stochastic resonance on Newman–Watts networks of Hodgkin–Huxley neurons with local periodic driving. Physics Letters A, Vol. 373, no 10, 2009, pp 964 – 968. doi:10.1016/j.physleta.2009.01.034 http://www.matjazperc.com/publications/PhysLettA_373_964.pdf

  106. A. Patel, B. Kosko: Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise. IEEE Trans. on Neural Networks, Vol. 19, no. 12, December 2008, pp 1993 – 2008. DOI 10.1109/TNN.2008.2005610 

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