Year |
Citation |
Score |
2017 |
Negrov D, Karandashev I, Shakirov V, Matveyev Y, Dunin-Barkowski W, Zenkevich A. An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity Neurocomputing. 237: 193-199. DOI: 10.1016/J.Neucom.2016.10.061 |
0.316 |
|
2015 |
Solovyeva KP, Karandashev IM, Zhavoronkov A, Dunin-Barkowski WL. Models of Innate Neural Attractors and Their Applications for Neural Information Processing. Frontiers in Systems Neuroscience. 9: 178. PMID 26778977 DOI: 10.3389/Fnsys.2015.00178 |
0.378 |
|
2012 |
Lovering AT, Fraigne JJ, Dunin-Barkowski WL, Vidruk EH, Orem JM. Tonic and phasic drive to medullary respiratory neurons during periodic breathing. Respiratory Physiology & Neurobiology. 181: 286-301. PMID 22484379 DOI: 10.1016/J.Resp.2012.03.012 |
0.356 |
|
2010 |
Dunin-Barkowski WL, Lovering AT, Orem JM, Baekey DM, Dick TE, Rybak IA, Morris KF, O'Connor R, Nuding SC, Shannon R, Lindsey BG. L-plotting-A method for visual analysis of physiological experimental and modeling multi-component data Neurocomputing. 74: 328-336. DOI: 10.1016/J.Neucom.2010.03.015 |
0.651 |
|
2008 |
Rybak IA, O'Connor R, Ross A, Shevtsova NA, Nuding SC, Segers LS, Shannon R, Dick TE, Dunin-Barkowski WL, Orem JM, Solomon IC, Morris KF, Lindsey BG. Reconfiguration of the pontomedullary respiratory network: a computational modeling study with coordinated in vivo experiments. Journal of Neurophysiology. 100: 1770-99. PMID 18650310 DOI: 10.1152/Jn.90416.2008 |
0.626 |
|
2006 |
Dunin-Barkowski WL, Sirota MG, Lovering AT, Orem JM, Vidruk EH, Beloozerova IN. Precise rhythmicity in activity of neocortical, thalamic and brain stem neurons in behaving cats and rabbits. Behavioural Brain Research. 175: 27-42. PMID 16956677 DOI: 10.1016/J.Bbr.2006.07.028 |
0.373 |
|
2006 |
Lovering AT, Fraigne JJ, Dunin-Barkowski WL, Vidruk EH, Orem JM. Medullary respiratory neural activity during hypoxia in NREM and REM sleep in the cat. Journal of Neurophysiology. 95: 803-10. PMID 16192335 DOI: 10.1152/Jn.00615.2005 |
0.35 |
|
2003 |
Dunin-Barkowski WL, Escobar AL, Lovering AT, Orem JM. Respiratory pattern generator model using Ca++-induced Ca++ release in neurons shows both pacemaker and reciprocal network properties. Biological Cybernetics. 89: 274-88. PMID 14605892 DOI: 10.1007/S00422-003-0418-6 |
0.343 |
|
2002 |
Dunin-Barkowski WL. Analysis of output of all Purkinje cells controlled by one climbing fiber cell Neurocomputing. 44: 391-400. DOI: 10.1016/S0925-2312(02)00386-7 |
0.421 |
|
2002 |
Dunin-Barkowski WL, Lovering AT, Orem JM. A neural ensemble model of the respiratory central pattern generator: Properties of the minimal model Neurocomputing. 44: 381-389. DOI: 10.1016/S0925-2312(02)00385-5 |
0.369 |
|
2000 |
Dunin-Barkowski WL, Wunsch DC. Phase-based cerebellar learning of dynamic signals Neurocomputing. 32: 709-725. DOI: 10.1016/S0925-2312(00)00236-8 |
0.373 |
|
1999 |
Dunin-Barkowski WL, Shishkin SL, Wunsch DC. Stability properties of cerebellar neural networks: The Purkinje cell - climbing fiber dynamic module Neural Processing Letters. 9: 97-106. DOI: 10.1023/A:1018634805731 |
0.413 |
|
1999 |
Dunin-Barkowski WL, Wunsch DC. Phase-based storage of information in the cerebellum Neurocomputing. 26: 677-685. DOI: 10.1016/S0925-2312(98)00156-8 |
0.338 |
|
1997 |
Dunin-Barkowski WL, Markin SN, Podladchikova LN. Estimation of purkinje cells complex and simple spikes contribution to local field potentials of the cerebellar cortex Biofizika. 42: 520. |
0.617 |
|
1995 |
Dunin-Barkowski WL, Osovets NB. Hebb-Hopfield neural networks based on one-dimensional sets of neuron states Neural Processing Letters. 2: 28-31. DOI: 10.1007/Bf02332163 |
0.343 |
|
1995 |
Dunin-Barkowski WL, Markin SN, Podladchikova LN. Climbing fiber Purkinje cells twins: A search of extra-rare events in neural systems International Rnns/Ieee Symposium On Neuroinformatics and Neurocomputers. 100-107. |
0.631 |
|
1993 |
Dunin-Barkowski WL, Khandozhko II, Podladchikova LN, Zherdeva SV. In search of cerebellar climbing fiber Purkinje cell twins Proceedings of the International Joint Conference On Neural Networks. 1: 121-122. |
0.654 |
|
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