Publications

Peer-reviewed Journal Articles

Reviews

Book chapters

  • Smolinski TG, Prinz AA (2012).
  • Rough Sets and Neuroscience. In: Skowron A, Suraj Z, eds. Rough Sets and Intelligent Systems. Springer, New York NY: 493-514.

  • Schultheiss NW, Prinz AA, Butera RJ, eds. (2012).
  • Phase Response Curves in Neuroscience – Theory, Experiment, and Analysis. Springer, New York NY.

  • Hooper R, Prinz AA (2011).
  • Biohybrid Circuits: The Dynamic Clamp. In: Jung R, ed. Biohybrid Systems. Wiley-VCH, Weinheim, Germany: 77-94.

  • Prinz AA, Smolinski TG, Hudson AE (2011).
  • Understanding Animal-to-Animal Variability in Neuronal and Network Properties. In: Ding M, Glanzman D, eds. Neuronal Variability and its Functional Significance. Oxford University Press, Oxford, UK: 119-138.

  • Achuthan S, Sieling FH, Prinz AA, Canavier CC (2011).
  • Phase Resetting in the Presence of Noise and Heterogeneity. In: Ding M, Glanzman D, eds. Neuronal Variability and its Functional Significance. Oxford University Press, Oxford, UK: 104-118.

  • National Research Council: Committee on Forefronts of Science at the Interface of Physical and Life Sciences (2010).
  • Research at the Intersection of the Physical and Life Sciences. The National Academies Press, Washington, DC.

  • Calabrese R.L., Prinz A.A. (2009).
  • Realistic modeling of small neuronal networks. In: De Schutter E, ed. Computational Modeling Methods for Neuroscientists. MIT Press.

  • Canavier C.C., Sieling F.H., Prinz A.A. (in press).
  • Dynamic-clamp constructed hybrid circuits for the study of synchronization phenomena in networks of bursting neurons. In: Destexhe A., Bal T., eds. Dynamic clamp: from principles to applications. Springer.

  • Prinz A.A. (2008).
  • Plasticity and stability in neuronal and network dynamics. In: Soltesz I., Staley K., eds. Computational Neuroscience in Epilepsy. Elsevier.

  • Prinz A.A. (2007).
  • Computational exploration of neuron and neural network models in neurobiology. In: Crasto C.J., ed. Methods in Molecular Biology: Bioinformatics. Humana Press, Totowa NJ: 167-179.

  • Smolinski T.G., Prinz A.A., Zurada J.M. (2007).
  • Hybridization of Rough Sets and Multi-Objective Evolutionary Algorithms for Classificatory Signal Decomposition. In: Hassanien A.-E., Suraj Z., Slezak D., Lingras P., eds. Rough Computing: Theories, Technologies, and Applications. Information Science Reference, Hershey NY: 204-227.

  • Abbott L.F., Thoroughman K., Prinz A., Thirumalai V., Marder E. (2003).
  • Activity-dependent modification of intrinsic and synaptic conductances in neurons and rhythmic networks. In: Van Ooyen A., ed. Modeling Neural Development. MIT Press, Cambridge MA: 151-166.

Commentaries

Encyclopedia Contributions

  • Marder E, Prinz AA, Abbott LF (2003).
  • Dynamic clamp: modeling with biological neurons. In: Adelman G, Smith BH, eds. Encyclopedia of Neuroscience, 3rd ed. Elsevier.

  • Prinz AA (2007).
  • Neuronal Parameter Optimization. Scholarpedia 2(1):1903.

  • Cudmore RH, Prinz AA (2011).
  • Dynamic Clamp. Scholarpedia 6(5):1470.

Peer-Reviewed Conference Contributions

  • Günay C, Prinz AA (2014).
  • Estimation of spike initiation zone and synaptic input parameters of a Drosophila motoneuron using a morphologically reconstructed model. BMC Neuroscience 15(Suppl 1): P65.

  • Hooper RM, Tikidzhi-Khamburyan RA, Canavier CC, Prinz AA (2014).
  • BMC Neuroscience 15(Suppl 1): P64.

  • Malik A, Prinz AA, Smolinksi TG (2014).
  • A system for automated analysis of conductance correlations involved in recovery of electrical activity after neuromodulator deprivation in stomatogastric neuron models. BMC Neuroscience 15(Suppl 1): P41.

  • Cymbalyuk GS, Prinz AA, eds. (2014).
  • Abstracts from the Twenty Third Annual Computational Neuroscience Meeting: CNS*2014. In: Shipley T, series ed. BMC Neuroscience 15(Suppl 1).

  • Malik A, Shim K, Prinz AA, Smolinski TG (2013).
  • 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(Suppl 1): P370.

  • Cymbalyuk GS, Prinz AA, eds. (2013).
  • Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. In: Shipley T, series ed. BMC Neuroscience 14(Suppl 1).

  • Archila S, Prinz AA (2012).
  • Investigating synaptic plasticity in the crab Cancer borealis pyloric circuit and in a computational pyloric model network database. BMC Neuroscience 13(Suppl 1): P69.

  • Soofi W, Prinz AA (2012).
  • Effect of intrinsic membrane conductances on Phase Resetting Curves in a conductance-based neuron model. BMC Neuroscience 13(Suppl 1): P68.

  • Tang C, Hudson AE, Prinz AA (2012).
  • Reducing the maximal calcium conductance in models of the pyloric network after decentralization prevents recovery. BMC Neuroscience 13(Suppl 1): P67.

  • Günay C, Dharmar L, Sieling F, Baines RA, Prinz AA (2012).
  • A compartmental model of an identified Drosophila larval motoneuron for investigating functional effects of ion channel parameters BMC Neuroscience 13(Suppl 1): P66.

  • Shim K, Prinz AA, Smolinski TG (2012).
  • Analyzing conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models. BMC Neuroscience 13(Suppl 1): P37.

  • Fellous J-M, Prinz AA, eds. (2012).
  • Twenty First Annual Computational Neuroscience Meeting: CNS*2012 – Meeting Abstracts. In: Shipley T, series ed. BMC Neuroscience 13(Suppl 1).

  • McKee L, Prinz AA, Smolinski TG (2011).
  • Improving visualization and analysis of relationships between neuronal model parameters in discrete parameter spaces. BMC Neuroscience 12(Suppl 1): P309.

  • Günay C, Prinz AA (2011).
  • An offline correction method for uncompensated series resistance and capacitance artifacts from whole-cell patch clamp recordings of small cells. BMC Neuroscience 12(Suppl 1): P259.

  • Günay C, Dharmar L, Sieling F, Baines R, Prinz AA (2011).
  • A novel model of an identified Drosophila crawl motomeuron for investigating functional effects of ion channel type across larval developmental stages. BMC Neuroscience 12(Suppl 1): P258.

  • Fellous J-M, Prinz AA, eds. (2011).
  • Twentieth Annual Computational Neuroscience Meeting: CNS*2011 – Meeting Abstracts. In: Shipley T, series ed. BMC Neuroscience 12(Suppl 1).

  • Smolinski TG, Prinz AA (2010).
  • Classifying functional and non-functional model neurons using the theory of rough sets. BMC Neuroscience 11(Suppl 1): P157.

  • Günay C, Dharmar L, Sieling F, Marley R, Lin WH, Baines RA, Prinz AA (2010).
  • Modeling Drosophila motoneurons to examine the functional effect of Na channel splice variants. BMC Neuroscience 11(Suppl 1): P147.

  • Soofi W, Prinz AA (2010).
  • Covarying ionic conductances to emulate phase maintenance in stomatogastric neurons. BMC Neuroscience 11(Suppl 1): P60.

  • Olypher AV, Lytton WW, Prinz AA (2010).
  • Transformation of inputs in a model of the rat hippocampal CA1 network. BMC Neuroscience 11(Suppl 1): P56.

  • Sieling FH, Simmers J, Prinz AA, Nargeot R (2010).
  • Changes in electrical coupling via dynamic clamp produces correlates of operant conditioning in the feeding CPG networks of Aplysia. BMC Neuroscience 11(Suppl 1): P1.

  • Smolinski TG, Prinz AA (2010).
  • Rough Sets for Solving Classification Problems in Computational Neuroscience. Rough Sets and Current Trends in Computing, Proceedings 6086: 620-629.

  • Sieling FH, Canavier CC, Prinz AA (2009).
  • Inclusion of noise in iterated firing maps based on the PRC. BMC Neuroscience 10(Suppl. 1): P345.

  • Hudson AE, Prinz AA (2009).
  • Activity-dependent conductance relationships in a model neuron database. BMC Neuroscience 10(Suppl. 1): P41.

  • Prinz AA, Olypher AV (2009).
  • Geometry and dynamics of activity-dependent homeostatic regulation in neurons. BMC Neuroscience 10(Suppl. 1): P203.

  • Smolinski TG, Prinz AA (2009).
  • Multi-objective evolutionary algorithms for model parameter value selection matching biological behavior under different simulation scenarios. BMC Neuroscience 10(Suppl. 1): P260.

  • Günay C, Prinz AA (2009).
  • Calcium sensor parameters and readout configurations for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion. BMC Neuroscience 10(Suppl. 1): O4.

  • Smolinski TG, Prinz AA (2009).
  • Computational Intelligence in Modeling of Biological Neurons: A Case Study of an Invertebrate Pacemaker Neuron. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, GA, June 14-19, 2009, 2964-2970.

  • Günay C, Prinz AA (2009).
  • Finding sensors for homeostasis of biological neuronal networks using artificial neural networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, GA, June 14-19, 2009.

  • Gurel Kazanci F, Maran SK, Prinz AA, Canavier CC (2008).
  • Predicting n:1 locking in pulse coupled two-neuron networks using phase resetting theory. BMC Neuroscience 9(Suppl. 1): 136.

  • Maran SK, Sieling FH, Prinz AA, Canavier CC (2008).
  • Predicting excitatory phase resetting curves in bursting neurons. BMC Neuroscience 9(Suppl. 1): 134.

  • Sieling FH, Canavier CC, Prinz AA (2008).
  • Predicting phase-locking in excitatory hybrid circuits. BMC Neuroscience 9(Suppl. 1): 133.

  • Smolinski TG, Soto-Trevino C, Rabbah P, Nadim F, Prinz AA (2008).
  • Systematic selection of model parameter values matching biological behavior under different simulation scenarios. BMC Neuroscience 9(Suppl. 1): 53.

  • Günay C, Hooper RM, Hammett KR, Prinz AA (2008).
  • Calcium sensor properties for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion. BMC Neuroscience 9(Suppl. 1): 42.

  • Langton JT, Prinz AA, Hickey TJ (2007).
  • Neurovis: combining dimensional stacking and pixelization to visually explore, analyze, and mine multidimensional multivariate data. In: Proceedings of SPIE: Visualization and Data Analysis 6495: 64950H1-64950H12, SPIE and IS&T.

  • Vargas, Prinz AA (2007).
  • Does reliable neuromodulation require that neuronal network parameters are tightly regulated? BMC Neuroscience 8(Suppl. 2): 195.

  • Smolinski TG, Soto-Trevino C, Rabbah P, Nadim F, Prinz AA (2007).
  • 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(Suppl. 2): 164.

  • Langton JT, Prinz AA, Wittenberg DK, Hickey TJ (2006).
  • Leveraging layout with dimensional stacking and pixelization to facilitate feature discovery and directed queries. Lecture Notes in Comput Sci 4370: 77-91.

  • Smolinski TG, Boratyn GM, Milanova M, Buchanan R, Prinz AA (2006).
  • Hybridization of independent component analysis, rough sets, and multi-objective evolutionary algorithms for classificatory decomposition of cortical evoked potentials. Lect Notes in Bioinform 4146: 174-183.

  • Smolinski TG, Buchanan R, Boratyn GM, Milanova M, Prinz AA (2006).
  • Independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials. BMC Bioinformatics 7: Art. No. S8 Suppl. 2.

  • Langton JT, Prinz AA, Hickey TJ (2006).
  • Combining pixelization and dimensional stacking. Lecture Notes in Comput Sci 4292: 617–626.

  • Smolinski TG, Soto-Trevino C, Rabbah P, Nadim F, Prinz AA (2006).
  • Analysis of biological neurons via modeling and rule mining. Int J IT & IC 1(2): 293-302.

  • Smolinski TG, Milanova M, Boratyn GM, Buchanan R, Prinz AA (2006).
  • Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials. Proceedings of the IEEE International Conference on Granular Computing (GrC 2006), Atlanta, GA, May 10-12, 2006, pp. 635 - 638.

  • Hong E., Taylor A.L., Prinz A.A. (2006).
  • Comparison of fast and slow tonically spiking neurons based on conductance space exploration of model neurons from two different phyla. Computational Neuroscience Meeting, July 16-20, 2006, Edinburgh, Scotland.

Conference Abstracts

  • Langton J.T., Prinz A.A., Hickey T.J. (2006).
  • NeuroVis: Exploring interaction techniques by combining dimensional stacking and pixelization to visualize multidimensional multivariate data. InfoVis 2006.

  • Buchanan R., Milanova M., Smolinski T.G., Boratyn G.M., Prinz A.A. (2006).
  • Decomposition and analysis of cortical evoked potentials using ICA. 3rd Annual MidSouth Computational Biology and Bioinformatics Society Conference (MCBIOS 2006), Baton Rouge, Louisiana, February 2-4, 2006.

  • Smolinski T.G., Prinz A.A., Soto-Treviño C., Rabbah P., Nadim F. (2005).
  • Computational exploration of a multi-compartment model of the lobster pyloric pacemaker kernel. Society for Neuroscience 35th Annual Meeting, Washington, D.C., November 12-16, 2005.

  • Smolinski T.G., Soto-Treviño C., Rabbah P., Nadim F., Prinz A.A. (2005).
  • Application of evolutionary algorithms-based pseudo-association rule mining to analysis of the intrinsic properties of the PD neuron in the lobster pyloric network. Second SECABC Fall Workshop on Biocomputing; Atlanta, Georgia, October 27, 2005.