| Year |
Citation |
Score |
| 2018 |
Kerr CC, Dura-Bernal S, Smolinski TG, Chadderdon GL, Wilson DP. Optimization by Adaptive Stochastic Descent. Plos One. 13: e0192944. PMID 29547665 DOI: 10.1371/Journal.Pone.0192944 |
0.4 |
|
| 2017 |
Fox DM, Tseng HA, Smolinski TG, Rotstein HG, Nadim F. Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents. Plos Computational Biology. 13: e1005565. PMID 28582395 DOI: 10.1371/journal.pcbi.1005565 |
0.458 |
|
| 2015 |
Smolinski TG, Lombardo J, Harrington MA. Analyzing adaptive modulation in spinal motor neurons using multi-objective evolutionary algorithms Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P94 |
0.433 |
|
| 2014 |
Malik A, Prinz AA, Smolinski TG. A system for automated analysis of conductance correlations involved in recovery of electrical activity after neuromodulator deprivation in stomatogastric neuron models Bmc Neuroscience. 15: P41. DOI: 10.1186/1471-2202-15-S1-P41 |
0.697 |
|
| 2013 |
Malik A, Shim K, Prinz AA, Smolinski TG. Multi-objective evolutionary algorithms for analysis of conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models Bmc Neuroscience. 14: P370. DOI: 10.1186/1471-2202-14-S1-P370 |
0.704 |
|
| 2013 |
Patel P, Johnson-Gray M, Forren E, Malik A, Smolinski TG. Hybridization of multi-objective evolutionary algorithms and fuzzy control for automated construction, tuning, and analysis of neuronal models Bmc Neuroscience. 14: P369. DOI: 10.1186/1471-2202-14-S1-P369 |
0.693 |
|
| 2013 |
Smolinski TG, Newell T, McDaniel S, Pokrajac D. Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets Proceedings of Spie - the International Society For Optical Engineering. 8857. DOI: 10.1117/12.2024580 |
0.706 |
|
| 2013 |
Smolinski TG, Prinz AA. Rough Sets and Neuroscience Intelligent Systems Reference Library. 43: 493-514. DOI: 10.1007/978-3-642-30341-8_26 |
0.514 |
|
| 2012 |
Shim K, Prinz AA, Smolinski TG. Analyzing conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models Bmc Neuroscience. 13: P37. DOI: 10.1186/1471-2202-13-S1-P37 |
0.674 |
|
| 2012 |
Forren E, Johnson-Gray M, Patel P, Smolinski TG. NeRvolver: a computational intelligence-based system for automated construction, tuning, and analysis of neuronal models Bmc Neuroscience. 13: P36. DOI: 10.1186/1471-2202-13-S1-P36 |
0.696 |
|
| 2011 |
McKee L, Prinz AA, Smolinski TG. Improving visualization and analysis of relationships between neuronal model parameters in discrete parameter spaces Bmc Neuroscience. 12: P309. DOI: 10.1186/1471-2202-12-S1-P309 |
0.642 |
|
| 2011 |
Prinz AA, Smolinski TG, Hudson AE. Understanding Animal-to-Animal Variability in Neuronal and Network Properties The Dynamic Brain: An Exploration of Neuronal Variability and Its Functional Significance. DOI: 10.1093/acprof:oso/9780195393798.003.0007 |
0.722 |
|
| 2010 |
Smolinski TG, Prinz AA. Classifying functional and non-functional model neurons using the theory of rough sets Bmc Neuroscience. 11. DOI: 10.1186/1471-2202-11-S1-P157 |
0.671 |
|
| 2010 |
Smolinski TG, Prinz AA. Rough sets for solving classification problems in computational neuroscience Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6086: 620-629. DOI: 10.1007/978-3-642-13529-3_66 |
0.503 |
|
| 2009 |
Smolinski TG, Prinz AA. Multi-objective evolutionary algorithms for model neuron parameter value selection matching biological behavior under different simulation scenarios Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P260 |
0.715 |
|
| 2009 |
Smolinski TG, Prinz AA. Computational Intelligence in modeling of biological neurons: A case study of an invertebrate pacemaker neuron Proceedings of the International Joint Conference On Neural Networks. 2964-2970. DOI: 10.1109/IJCNN.2009.5178722 |
0.596 |
|
| 2008 |
Smolinski TG, Soto-Treviño C, Rabbah P, Nadim F, Prinz AA. Systematic selection of model parameter values matching biological behavior under different simulation scenarios Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P53 |
0.665 |
|
| 2008 |
Günay C, Smolinski TG, Lytton WW, Morse TM, Gleeson P, Crook S, Steuber V, Silver A, Voicu H, Andrews P, Bokil H, Maniar H, Loader C, Mehta S, Kleinfeld D, et al. Computational intelligence in electrophysiology: Trends and open problems Studies in Computational Intelligence. 122: 325-359. DOI: 10.1007/978-3-540-78534-7_14 |
0.599 |
|
| 2007 |
Smolinski TG, Prinz AA, Zurada JM. Hybridization of rough sets and multi-objective evolutionary algorithms for classificatory signal decomposition Rough Computing: Theories, Technologies and Applications. 204-227. DOI: 10.4018/978-1-59904-552-8.ch010 |
0.588 |
|
| 2007 |
Smolinski TG, Soto-Treviño C, Rabbah P, Nadim F, Prinz AA. Systematic computational exploration of the parameter space of the multi-compartment model of the lobster pyloric pacemaker kernel suggests that the kernel can achieve functional activity under various parameter configurations Bmc Neuroscience. 8. DOI: 10.1186/1471-2202-8-S2-P164 |
0.586 |
|
| 2006 |
Smolinski TG, Buchanan R, Boratyn GM, Milanova M, Prinz AA. Independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials. Bmc Bioinformatics. 7: S8. PMID 17118151 DOI: 10.1186/1471-2105-7-S2-S8 |
0.581 |
|
| 2006 |
Smolinski TG, Boratyn GM, Milanova M, Buchanan R, Prinz AA. Hybridization of independent component analysis, rough sets, and multi-objective evolutionary algorithms for classificatory decomposition of cortical evoked potentials Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4146: 174-183. |
0.582 |
|
| 2006 |
Smolinski TG, Milanova M, Boratyn GM, Buchanan R, Prinz AA. Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials 2006 Ieee International Conference On Granular Computing. 635-638. |
0.589 |
|
| 2002 |
Smolinski TG, Boratyn GM, Milanova M, Zurada JM, Wrobel A. Evolutionary algorithms and rough sets-based hybrid approach to classificatory decomposition of cortical evoked potentials Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2475: 621-628. DOI: 10.1007/3-540-45813-1_82 |
0.413 |
|
| 2002 |
Milanova M, Smolinski TG, Boratyn GM, Zurada JM, Wrobel A. Sparse correlation kernel analysis and evolutionary algorithm-based modeling of the sensory activity within the rat’s barrel cortex Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2388: 198-212. DOI: 10.1007/3-540-45665-1_16 |
0.437 |
|
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